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2025 updates","body","# Add model factory + merbench eval updates\r\n\r\n## Summary\r\n- Introduce a centralized model factory with explicit provider handling (including OpenRouter) plus a new demo and documentation for multi-provider usage in the basic MCP examples. \r\n- Expand Merbench evaluation tooling with per-case debug trace capture, new request-usage tracking, and updated evaluation guidance (including OpenRouter examples).\r\n- Add Merbench utilities for merging processed benchmark outputs and verifying retry orchestration, plus preprocessing updates to normalize model names for cost lookups.\r\n- Refresh model pricing data, add OpenRouter env configuration, and pin the Mermaid CLI version used by the validator.\r\n\r\n## Why\r\n- OpenRouter makes multi-provider demos and evaluation runs possible.\r\n- Merbench debugging and reporting improvements (debug traces + request usage tracking + cost normalization) make it easier to diagnose tool loops, retries, and cost accuracy across providers.\r\n\r\n## How to Run / Verify\r\n- Run the new model-factory demo:\r\n - `uv run agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py --list-models`\r\n- Run multi-model Merbench with OpenRouter:\r\n - `uv run agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py --models \"openrouter:anthropic/claude-3.7-sonnet,gemini-3-flash-preview\" --runs 5 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is a ready-to-paste PR description based on the changes in this branch.\n\n---\n\n**Proposed PR Title**\n- **feat: add model factory + merbench eval updates**\n\n**PR Body**\n## Summary\n- Introduce a centralized model factory with explicit provider handling (including OpenRouter) plus a new demo and documentation for multi-provider usage in the basic MCP examples.【F:agents_mcp_usage/factory/model_factory.py†L1-L431】【F:agents_mcp_usage/factory/MODEL_FACTORY.md†L1-L200】【F:agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py†L1-L216】【F:agents_mcp_usage/basic_mcp/README.md†L228-L253】\n- Expand Merbench evaluation tooling with per-case debug trace capture, new request-usage tracking, and updated evaluation guidance (including OpenRouter examples).【F:agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py†L60-L122】【F:agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py†L319-L516】【F:agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py†L1151-L1219】【F:agents_mcp_usage/evaluations/mermaid_evals/README.md†L60-L444】\n- Add Merbench utilities for merging processed benchmark outputs and verifying retry orchestration, plus preprocessing updates to normalize model names for cost lookups.【F:agents_mcp_usage/evaluations/mermaid_evals/scripts/merge_benchmark_results.py†L1-L240】【F:agents_mcp_usage/evaluations/mermaid_evals/scripts/verify_retry_orchestration.py†L1-L126】【F:agents_mcp_usage/evaluations/mermaid_evals/scripts/preprocess_merbench_data.py†L1-L145】\n- Refresh model pricing data, add OpenRouter env configuration, and pin the Mermaid CLI version used by the validator.【F:agents_mcp_usage/evaluations/mermaid_evals/costs.json†L1-L570】【F:.env.example†L1-L6】【F:mcp_servers/mermaid_validator.py†L50-L88】\n\n## Why\n- Centralizing provider/model creation (including OpenRouter) makes multi-provider demos and evaluation runs consistent and configurable while enabling OpenAI Responses normalization and provider-specific handling in one place.【F:agents_mcp_usage/factory/model_factory.py†L1-L431】【F:agents_mcp_usage/factory/MODEL_FACTORY.md†L1-L200】\n- Merbench debugging and reporting improvements (debug traces + request usage tracking + cost normalization) make it easier to diagnose tool loops, retries, and cost accuracy across providers.【F:agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py†L60-L122】【F:agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py†L319-L516】【F:agents_mcp_usage/evaluations/mermaid_evals/scripts/preprocess_merbench_data.py†L23-L145】【F:agents_mcp_usage/evaluations/mermaid_evals/README.md†L378-L444】\n\n## How to Run / Verify\n- Run the new model-factory demo:\n - `uv run agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py --list-models`【F:agents_mcp_usage/basic_mcp/README.md†L234-L246】\n- Run multi-model Merbench with OpenRouter:\n - `uv run agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py --models \"openrouter:anthropic/claude-3.7-sonnet,gemini-3-flash-preview\" --runs 5 --sequential`【F:agents_mcp_usage/evaluations/mermaid_evals/README.md†L80-L90】\n- Merge processed Merbench outputs:\n - `uv run agents_mcp_usage/evaluations/mermaid_evals/scripts/merge_benchmark_results.py -i results/run1.json results/run2.json -o results/merged.json --dedup keep-all --report merge_report.json`【F:agents_mcp_usage/evaluations/mermaid_evals/README.md†L116-L124】\n- Verify retry orchestration logic (offline stub runner):\n - `uv run agents_mcp_usage/evaluations/mermaid_evals/scripts/verify_retry_orchestration.py`【F:agents_mcp_usage/evaluations/mermaid_evals/scripts/verify_retry_orchestration.py†L1-L126】\n\n## Schema / Output Impacts\n- Merbench CSV output now includes a request-usage column (`Requests_Used`).【F:agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py†L60-L70】【F:agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py†L1151-L1219】\n- Optional per-case debug traces are emitted to `/debug_traces/` when enabled, containing full prompts and tool-call payloads (sensitive data).【F:agents_mcp_usage/evaluations/mermaid_evals/README.md†L378-L444】\n- `merge_benchmark_results.py` outputs the same schema as `preprocess_merbench_data.py` (stats/leaderboard/pareto/raw_data/etc.).【F:agents_mcp_usage/evaluations/mermaid_evals/scripts/merge_benchmark_results.py†L1-L18】\n- Pricing inputs updated in `costs.json`; include the new OpenRouter env var template (`OPENROUTER_API_KEY`).【F:agents_mcp_usage/evaluations/mermaid_evals/costs.json†L1-L570】【F:.env.example†L1-L6】\n\n---","output_text","item/completed",{"_670":683,"_662":663,"_672":81},{"_41":684,"_676":-5,"_45":679,"_90":685},"legacy_agentMessage_65","agentMessage","item/started",{"_670":688,"_662":663,"_672":81},{"_41":684,"_676":-5,"_45":315,"_90":685},{"_670":690,"_662":663,"_672":81},{"_673":-5,"_691":692,"_41":693,"_694":695,"_90":696},"encrypted_content","gAAAAABpaAkcDsyE1st6yWFZE6aXgAQcK49zeccPUDoEVRBQsffpvCVPvtOCtxMoM53mbUX4FYlJfw86KF1gUh8LMIlRTNts3QFT7JdfcXbpLv88ATdEK1AdKMiaRR0bnlhSdVRcW3pplEYzBVoU4ISgrNULBb98MfmVE2jJSC8I17WK3N8WJG5urWUlWY_v5uFi2H1LAq1f7nViaRlyyei8qlxzI7g8Czll9ZvH2IH_8mPLDCXWOAClX-U2KCNumBs51qTpEP_oTWMIpPHRhcZ0n9dHzMLiJxjDXF7X-KNzmXnYuLFGIyRytce0dWwhLA1DQmTl0VchgpD0w4w8pJH25uDef9rr7MykzP36w0Pk_740W-fIuhh_JbFR8XnOLSoP6YlIsAETwVi_vMUpocrmIZxZzROGAmTzjDlRBlUDfrwMJ5tVDtZhm45_83Ux-aZ4DR0Wv7KzUhHqpMQEaGbNqPlrDW1B22L4r3hoaKXZext-j7RMQ2EnaEqyfg_46o6pORPI3kEhLBgp6kEkCADlB16EGiwIOITRIFm1GRtuY7nj0zHUJcPemelSY0s65Ovyh79noz6p-g5Omk4JXU1SG5GpNfHyS07PSRWH48MN5Ze6QmZKo0syNY5otUwOgu1tgsPa-5j5ZsCtso2e0AWW1JVWPBN45RENMeSWr4qNBlUBC1EC517ppLM6eEbk9ONNavL0B_-JmgdNsBw1PHiGN36AxET3f7ANISuaPxrb5OXPmyc-MKKWLVkn0uyhrKRnK-4lkcAJJ5AbpKFZRGnR8aLhP4QW2nFLTLT7KpzCfZ72_C-A5ykkMqNYYGs9j20lmVtDIOVhapsChHylL61FvHfPLds9ATIvC9yfe6te7CTP--hnFn5oh8McmD6m7VYIp3iBQai1QjyvK9YbWvmCqkILDseUEOQ0eCCpkW9vRYn5Y3U-tDZHN9UBgCadX1iPsKNRhwy9cxnRdXn9YSF0JHGX94OO3BtDEsiXap4UxOIv789v7Fvd5dMGyR5tNBWYVvNpvJbhUltLY9bjyY8hLNezKpyI0XN2kNQWLZ6el-uFQH-qo1wO_g3mpzRfxSGrMo52wTeZfPf21L0k2Wtez5lyaT8wDuo2pvA3G_scDZKq4tVOSDXiGPWVC_4MEWW0DnKlp2jZBd4L4mdPPIXyMXHV0aRrK9nzsVXqheg8bMuDRDRK-iK2uFOO1avqmQfk-nUxvF0ER3k5j3TX87Xvb9XIy4_3gOv0-yzilIhjeBwJTVhSr9aeuU8YS1JnLe1fgUByoXzX5EGNbxyTayPDVbLuZiqec23wzX1zwM4vLjtrkFlDOwWbTW5NU2U65M9_eWs5QqAfD6GZWSeWhpqrWbDxp3YHdnGepEOLQ5o2BvCPhowMsENepoXk1wAYv_ndg7i0QDzyey2AsMZ-1eyI-Zo378Quui6mGB0netR3zfHERTrJ39sQqNEZ6HABiWYedDVchHjHaLDOwigT3nXME2JkUk4Xw_aU2tHtJ3NiXOyHyml5uT_9kYW7P0bGwU5AEfleLUWQdYobIv9bUhSMNvJwzvBtIh5qU9lkF5gQU11pg90IwqoQJve8afg2fgpmXb7NBd4Pc__k8I_TezfO0ih6l3DwndTLdbrPafbPFJLuO__dskhdUkk-4eQWqEtkoJ6upWODhE3ujraARkTdcmHFMHJ-NSq-_6AC6YG39JvfO2qNzO7dX4c6TdFUNd5q3Bi0solQVmywFPknAf74x8kaKwmeUxVjz-j2WT4lZtg3QJJuSJrow6lUZs_0cLfrmxf104GHexyzkO6zFu_qzZGAw1-aCNAHhB7trn8SZRLi-oEOFdpcUucJ16cCEijkh5-_s99QsZ0M4nrwL7W-ugD4srhP2TqHyFEAgHweav4-mBJ6uXBGgRmlfPANwMXAWt_mqIMX_Fb4zIPhTGj9d9IfmDUS1sHRvhTG1gDWIuB9mIoxkBZWX-m_AXxSnTSoNL2B0C5KPApmkbRSDceX_mnmBdBYsr115bIpXA695p0ODOgTUZ8zUX1nx8z89VUCjM6pagsz5J1d4HS7TGJPfkv54UJGc3dczxZg9Lr9SvTOojCNM6Iwr_bdcg30rGHTMbctajh03z-tTQfyR2es5YCUS3LzzA4JhBRIC2RdpaIertba3OhQBIRVVGgiLR7HW1An_Wk7_JAifjSDHpVrO9Dk2GqOgC_RXymnYjgknNwx05g4mtAJyYYAnGRzb2MN1WOL9raameiz_AsfkmIyk4_2m9ae6_MbCZSwkkbA4Wn_RQRCurBfVW_-wHXTpu9ccfYs-VMDCQjVbv--oPL62OUsWMlS7kYOZYBB-moxsSoKWwXbVSGbornZdTeS9hr3hefWyS-54x6MHeN2ORuXVDPB1mtFXGp9ftotEF-ttMyu7P1DiKXn-dQXmBFGLu2m3VAIwCM9EFdi-7hQ-UgWudyd0L3EDLKuOAbCuSmxDW82lzfV8bYz-3vt62JYD8p7tcDjScKhB3KTLKopeaJlSuibjyDMykY5LcSbtlwfgMQnk92H8RZd3QzOBVbzpbOi4Kngr3a0EBuzcZ4PtLsi3OA3yE3htyr-0WYFcgrncSxfz6WGdmqJeUcZ2oc7MAWJuxt1eNVcEUU9bkZ29SzHynOsopIFTYSMv08u2-g9Mvx_UwogfIiIfNKJ26NyRCp_tNZ7PmcXRArmgYtY2Rq_fC_E0Ih3w2R88utReuWkE-ASyOjffXCUb6lo5mdv4DK3A8gZYzCdokvvWtfeYlesPJMPaOSZAoA2onpQ8RHCOn4cBbJWsOOegFHefDXKtt6NDNVMPgauKH9T6K0AkHJ_X3x4W0S6spTrKgl4InrMVHzNfqoqjcVhNEMKMPePyJbr7bv5iEmMNlLfhP0i7MQdumytuRS_bRImz8DwZkUO1KIYFF3JFGV6xQ4cIDjv3JRiSvkxdFifAjwv_n5iOjS-X_KrKJc4HC2_1L9OUGRcj5pZfcgWu_5YSD5yWTTdTTJvHyej3HR2gu-QqpiUaY8KJsBFMBYhrM6HZiEgdz8NMOPsWAPSg5vx5FyYeF-c1cUJ4oZwFhIsQQq2XHuRcFvZ93jFDxBpuudY_0rkr6SwaZLa3H_-EwONhRzVu_7t4GtBMYcG64ly9SsICWS86NksAnmmhnW2AbrItx6yow8xeqQnjqrdbZ5MSL8EAcIQOROQA1JcZOmLjjAZ-QSbtP-GX9R86Jy2V0Klnxg-w3A-ybmp3hRigOuSjoDm6_xo-Fu3xZwTjRwd5x7aoI2XgJ35GO8Pd9lJxIbDe4ZJvMarkz0NQKdocyJzcGfdz1HwY1YF4w76Gmw9hruB94b_EW6dm92LKdTEMv_AmfQbJicnpxECCEmSoPUyJs11mPO9qbqeLZEHciKSzhwANOX8K_PrErEcF7ubUYoSFSm2-wb-46W2lQ7bzxQ2WYayhRoMjq8odU6YPPIkIaV0UfXFTXEPthzec8zzMFkEclTf6WRe1cGruS9_9fQzUSRVyPpcjXtsEntaPJTfxAnuHeFu13OJW2PcVxZUtL3tHuy033PerMViDnK1ba5WpaYIHPbtVNbXtHxTAC38Vfy1y_mIGI-KQJy-rmHDYUhM2ulhF9MCtawgF_NLbSFt7Z9dCJRE8RVC-L59_EORDS0UpB7pNP6BvJq9bXQfsqT6AtjBhB75gCdYt3PMNF5wZD8M9zc4hU0l5BZ0NC42w4oTEf0UEL_IYL8wqfNiFON6lClG-mj7Tq0q5GttfcL298iJ1iyIuJTfM1bDi7w45MDw3hhmjv4w0oq44PcrjWRrxig_9AaLMOp3IQEtdfsgrXPbEpjkeRieZN700a1PahD8TDYYUXm60ENT7C7rT4jc-1icVxoDcZawtYedmXgnxOB074BQnT8_b_grCDFCwcZ_GH5TOwyw4Vnk0fyTMkIbY2PtLv655BqhGs5dcIMqHE9qYJRMAknBuviIzhuhMEw6jroXmtmIxoRj7dnWFFbw577FhtncJEfuDaMntJSmqPrlmBBqNjbJo62lVtbsaaGkFvyN92J9AruYmGgCe0GI2nXS3quY1kwSdN1g37L5jvoFOpjrKw3ZI7Lab-XoZzwE5lQAfUojKzLZgY9Zl6qHd2ZQt-FqsjwbeKUErpV2Dly_b30xYbbKdf-Mv1rxljp0T0mGW98G4cFaOWW9jZLCl3L6fRhsG9646_uVG_-iC_pC_5LCknG_5lg67HxUDUJiyQPFhdOydHw4ymem4BqZEu6BpLv_p2sVBUDZplMxz3CEXB3D8-OkxhSrqAl_cLtBhMVV6IG0bcQwrCsO6pptW857G1ZyWcXngEjYLx9gbQFRYZpMWyFKhucyG1GxJfxrIi9Ls0-DWpeM582fn9JIm-9aYM_KGnYr5ffJVa25zf8F500chAHYp3xdHUYQSEYvawJgbv1q8A9tjebUvh8xYWmaO61fZJufgs33s0aAYyo66V7wLCJogNo7U-BSUeFPV5Y2VqiV57qALPyNYV7-pJHFXuITGmYuupLg72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detailed PR body with citations**\n\nThe PR body now includes a comprehensive summary, motivation, testing instructions, and output/schema impact details, each citing relevant documentation and code sections for clarity and completeness.","summary_text","**Preparing PR description with citations**\n\nI'm compiling a detailed PR description including debug trace instructions, the new CSV column addition, the JSON merge script, and environment updates, citing relevant sections from evals_pydantic_mcp and README files for clarity.",{"_670":703,"_662":663,"_672":81},{"_673":704,"_41":705,"_694":706,"_90":696},[699],"legacy_reasoning_63",[],{"_670":708,"_662":663,"_672":81},{"_673":709,"_41":705,"_694":710,"_90":696},[],[],{"_670":712,"_662":663,"_672":81},{"_673":713,"_41":714,"_694":715,"_90":696},[701],"legacy_reasoning_62",[],{"_670":717,"_662":663,"_672":81},{"_673":718,"_41":714,"_694":719,"_90":696},[],[],{"_670":721,"_662":663,"_672":81},{"_722":723,"_724":725,"_90":726},"call_id","call_9vQs99Fd0rcOQH1TR0Ntchmh","output",{"_408":727,"_728":-5},"function_call_output","Chunk ID: 5c9182\nWall time: 0.0514 seconds\nProcess exited with code 0\nOriginal token count: 751\nOutput:\n 210\t- Includes Logfire instrumentation for both MCP and OpenAI Agents\r\n 211\t\r\n 212\t### Pydantic-AI\r\n 213\t\r\n 214\t**File:** `pydantic_mcp.py`\r\n 215\t\r\n 216\tThis example demonstrates how to use MCP with the Pydantic-AI agent framework.\r\n 217\t\r\n 218\t```bash\r\n 219\tuv run agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp.py\r\n 220\t```\r\n 221\t\r\n 222\tKey features:\r\n 223\t- Uses the simplified Pydantic-AI Agent interface\r\n 224\t- Configures MCPServerStdio for MCP communication\r\n 225\t- Employs context manager for server lifecycle management\r\n 226\t- Includes comprehensive instrumentation for both MCP and Pydantic-AI\r\n 227\t\r\n 228\t### Pydantic-AI with Model Factory Pattern\r\n 229\t\r\n 230\t**File:** `pydantic_mcp_factory.py`\r\n 231\t\r\n 232\tThis example demonstrates how to use the model factory pattern to create agents with different providers while maintaining the same MCP functionality.\r\n 233\t\r\n 234\t```bash\r\n 235\t# Use default Gemini model\r\n 236\tuv run agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py\r\n 237\t\r\n 238\t# Use DeepSeek model\r\n 239\tuv run agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py --model deepseek\r\n 240\t\r\n 241\t# Use OpenAI model with explicit provider\r\n 242\tuv run agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py --model \"openai:gpt-4\"\r\n 243\t\r\n 244\t# List available model aliases\r\n 245\tuv run agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py --list-models\r\n 246\t```\r\n 247\t\r\n 248\tKey features:\r\n 249\t- Demonstrates the model factory pattern for multi-provider support\r\n 250\t- Supports 13+ providers including Google, OpenAI, Anthropic, AWS Bedrock, DeepSeek, OpenRouter, GitHub, Grok, Perplexity, and local Ollama models\r\n 251\t- Command-line interface for easy model switching\r\n 252\t- Shows how to handle provider-specific configurations\r\n 253\t- Maintains the same MCP server functionality across all providers\r\n 254\t\r\n 255\t\r\n 256\t## Understanding the Examples\r\n 257\t\r\n 258\tEach example follows a similar pattern:\r\n 259\t\r\n 260\t1. **Environment Setup**: Loading environment variables and configuring logging\r\n 261\t2. **Server Connection**: Establishing a connection to the local MCP server\r\n 262\t3. **Agent Configuration**: Setting up an agent with the appropriate model\r\n 263\t4. **Execution**: Running the agent with a query and handling the response\r\n 264\t\r\n 265\tThe examples are designed to be as similar as possible, allowing you to compare how different frameworks approach MCP integration.\r\n 266\t\r\n 267\t## MCP Server\r\n 268\t\r\n 269\tAll examples connect to the same MCP server defined in `mcp_servers/example_server.py`. This server provides:\r\n 270\t\r\n 271\t- An addition tool (`add(a, b)`)\r\n 272\t- A time tool (`get_current_time()`) \r\n 273\t- A dynamic greeting resource (`greeting://{name}`)\r\n 274\t\r\n 275\tYou can modify the MCP server to add your own tools and resources for experimentation.\r\n","success",{"_670":730,"_662":663,"_672":81},{"_731":732,"_733":734,"_735":736,"_737":738,"_739":740,"_741":31,"_41":723,"_742":743,"_744":745,"_667":111,"_90":746},"aggregatedOutput"," 210\t- Includes Logfire instrumentation for both MCP and OpenAI Agents\r\n 211\t\r\n 212\t### Pydantic-AI\r\n 213\t\r\n 214\t**File:** `pydantic_mcp.py`\r\n 215\t\r\n 216\tThis example demonstrates how to use MCP with the Pydantic-AI agent framework.\r\n 217\t\r\n 218\t```bash\r\n 219\tuv run agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp.py\r\n 220\t```\r\n 221\t\r\n 222\tKey features:\r\n 223\t- Uses the simplified Pydantic-AI Agent interface\r\n 224\t- Configures MCPServerStdio for MCP communication\r\n 225\t- Employs context manager for server lifecycle management\r\n 226\t- Includes comprehensive instrumentation for both MCP and Pydantic-AI\r\n 227\t\r\n 228\t### Pydantic-AI with Model Factory Pattern\r\n 229\t\r\n 230\t**File:** `pydantic_mcp_factory.py`\r\n 231\t\r\n 232\tThis example demonstrates how to use the model factory pattern to create agents with different providers while maintaining the same MCP functionality.\r\n 233\t\r\n 234\t```bash\r\n 235\t# Use default Gemini model\r\n 236\tuv run agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py\r\n 237\t\r\n 238\t# Use DeepSeek model\r\n 239\tuv run agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py --model deepseek\r\n 240\t\r\n 241\t# Use OpenAI model with explicit provider\r\n 242\tuv run agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py --model \"openai:gpt-4\"\r\n 243\t\r\n 244\t# List available model aliases\r\n 245\tuv run agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py --list-models\r\n 246\t```\r\n 247\t\r\n 248\tKey features:\r\n 249\t- Demonstrates the model factory pattern for multi-provider support\r\n 250\t- Supports 13+ providers including Google, OpenAI, Anthropic, AWS Bedrock, DeepSeek, OpenRouter, GitHub, Grok, Perplexity, and local Ollama models\r\n 251\t- Command-line interface for easy model switching\r\n 252\t- Shows how to handle provider-specific configurations\r\n 253\t- Maintains the same MCP server functionality across all providers\r\n 254\t\r\n 255\t\r\n 256\t## Understanding the Examples\r\n 257\t\r\n 258\tEach example follows a similar pattern:\r\n 259\t\r\n 260\t1. **Environment Setup**: Loading environment variables and configuring logging\r\n 261\t2. **Server Connection**: Establishing a connection to the local MCP server\r\n 262\t3. **Agent Configuration**: Setting up an agent with the appropriate model\r\n 263\t4. **Execution**: Running the agent with a query and handling the response\r\n 264\t\r\n 265\tThe examples are designed to be as similar as possible, allowing you to compare how different frameworks approach MCP integration.\r\n 266\t\r\n 267\t## MCP Server\r\n 268\t\r\n 269\tAll examples connect to the same MCP server defined in `mcp_servers/example_server.py`. This server provides:\r\n 270\t\r\n 271\t- An addition tool (`add(a, b)`)\r\n 272\t- A time tool (`get_current_time()`) \r\n 273\t- A dynamic greeting resource (`greeting://{name}`)\r\n 274\t\r\n 275\tYou can modify the MCP server to add your own tools and resources for experimentation.\r\n","command","/bin/bash -lc 'nl -ba agents_mcp_usage/basic_mcp/README.md | sed -n '\"'\"'210,290p'\"'\"''","commandActions",[747],"cwd","/workspace/agents-mcp-usage","durationMs",51,"exitCode","processId","87133","source","agent","commandExecution",{"_733":748,"_297":749,"_216":750,"_90":751},"nl -ba agents_mcp_usage/basic_mcp/README.md | sed -n 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'210,290p'\"}","exec_command","namespace","function_call",{"_670":765,"_662":663,"_672":81},{"_673":-5,"_691":766,"_41":767,"_694":768,"_90":696},"gAAAAABpaAkPT16b33srzIhLBbfUxaxMqkcWl3zY6FpszrUlttw17-3QZMH1bbIf6Psv8Ttpe_S19Rvp4ksKxrfl2HPL6pRFVcTnEeHV2WF2_uTQpO-Gd0OiPAd15vkwVsWusbg9Sbf9qOAMgeg-abCUaHgQoeQ7z092uX2D93SJzCZkVAU5f_UxOdtE1yPvHFa1hcvorEabwTIQ1e_BxiTLPFlCpEfbV7NTdvZbVihUdNeK88ONCBUCtn0_-IuGeJjse0Lav2wPFUkdt23XW06a57aBE6cWfpI7hHJL3Xzw4E3dXGk6W_ufCXFQB3k4KT22QcQNlbUbIczDxg2ICot8slLwBalAGXm3QBu3P9we9HIiXwIXHNSaNvx6wzDScIQ5j0iyFoAAbEifIg0XSunlWtVSdxbRX0J-bZ72myLcUDioQ8RtDM0MVHxZCJ_cS07X6T28bwdOBDRBxWcDK8y3jeupaCOixHhgRqjHW4rtRp3MgTf9byIdBGLZvYNN7IaBJZw2sjrbHvj6IbSzsZO-o9YpNhnI57dfIK3lht-uLuokESu9WruFUahnn4ULp2C5TBz7oakrarzKodsYnagXTu_NsO85b4pCmkFLpZeUTYXGrTbnML3tXTCqwelKEyL13kI40EwgsX_OTOQLwHB_qSSPd90px9MQGDs9XkC3VI3v_5mIWew761AlZKsfB4ITtrTePa4CEuNXgDTyCzQlZVh8SlTQyPZa5iWnOPxRLgjQe0ugsS-_v-nFbPyPmst53Vj0Vqd8iW20BOinRP36Ock3hS35cWcV-dkDoanvv5Qq0yEZ516I9YEjoT_QyZsRV8ybdjvX5HkAgQQQAk2DJg88QJigk5PFC77EImeEzMu9Jm7I54BXwrQ9Ai36KCNBF7mu0gJKG9fHldllkv-WxqCUmpE9Fnzg1s0Et7TVw6bnSvB-8k2c94K5-AoaGwtcTQ955BHDsFJ_MRwOuvDkGiYLdayxWI9CRlJMNfEX0nSYoI7SJhKhttLvgLuikIVnisnwrDI9_d67e0WkRIPNcyONEyraAzwVwvK-hdsu6GVS6NEePnx_6JaYkckSPGaNJs50EjHkvLaj_c4q_5Ki8pOTm9mBcXif0zXWTd3lW3P8nO2WKVOheP90FkakR8w_pqzBenYzfyKQXJ7dAK5OQXz3WAPIDyS6UQtIlncrhqF9pVKMaZHT5EtLSHDtATRaQg41fVbaqRjBF1-lO8Y3BDJxQv4mAgJ15C0J2LhPDAGKapAGw4Zu7RItqa3L1v5qNFKtxCmt0xM3tfum5s4Ka_5WG0VSgA33ttOz7Vl9zQtjLJHWSPRJ6M2wjvcsjfj8I_1jn-B529riA6MLgEVa96jhYmXYTYiZexICNBD026GIH3g3MRILhZhf5m_uHrMQflUsVbVF9LF-Nbm8b35aT8NGrittU4noeMemExY-KjChfZtAwU3ggIY3q9O_At-nnKUUXgaxhSOzUF6noJuSOHmTOBiqneHLID9K7RiaE1rSGqtwW7HqO-Yr0-1OznLJ5H98IFBVmamI2_yoCOIaecMQLX4JiT8EL9u-bd_76o8OFHeUcoH8v2g_Koi8K1vznjMV_UUGGuIEI6-basFVu2Ew5HANCbgIw-1H3HtP-jWLftWp2BWaWN9wEVXoxAz0AmFYpYdFP7iXMLBV5KtW_pDGHEC_f3SlquxHKWl4WsOl-n_VORPlwt3NMGTHvpxL-WjkSa2mHOxTFl_mst3jp5llxGWMfp6h_JN26y01Xbmy6YOpBBhYPCYzb1b7NoJx15AzleYVjqR92i5rqgDwKrafXCu75imoTW_kt7BVlMwZ0sHhwH-lACiKEEMGDHr0DuCx0O-I-bebirnB0SSN0bk3ZTxm6EQ9AKyu4eHFInzaIzI1NRy_9u15tPlT3elXyRAlyESFrL0Ml5f0-VMKATeimh462WvBQrxvJSfnbuy5krUSQ-9d6uFS0ZphmAsBznD6c-IQI1CO02t4xoLkc4ti1SycpmAbjuxhfNBkW3gkQwUOLvb4gouTL5nk0uUdTbjiJbNUZ4t5Bjmb17wQtm5DOU15ksjFl0du9l5I7cZke4wrM5gymU9K6GFuHTg2dDivVjkorTRW2Fnsp6lma6Ymgb1mC4ovpx0PyatCDtZ_zMszZ1XsS2PoGjP47Sj4Z7YhrTj6hLzaBh-ztgX084_VoZApXyFnk_zs1CP3Bf6bpbZvc97hYbbkCWcDvsM1mq3lW6QGpdwwRKmn0oBkysVTwYQX3BENXfOyLXOtwmEAHyNd6Z1dFyjnJ5l3qf92Mq5_bBePm_9O5dcXCW2RrxzntDGdS8DbfbEtXc3yiaC9mF-83teEx61IEF1nYbMNBb2nfRD30Re0pFqCj4t_fEqvqZcQZYolSIp5AHzEJyz6zACwS55Pu8zMdWvwco4sKvwCNSIXMMj2JYIrhLBB9rNSjS0J1uerXxF0nmWAjGamjuI3TEJmAni7XshHy4fcdYjpaqS98twDH-FWvjUUbBQCqN1Ox0cz0ZbKGJD4hJEt88qRTDO6HLH_SlLcuDE_TAPMRxLDXMLwXkmb0goZe7EnILyCXchJKkVqo3d_kd5YRkL0vGeH7JnZFNbtq2D0c8zf-gzItatRdewy8H4Z9togoLuFfC0-lnp-XMmkrn5F5by2RRTFHg2VKO2o-CujBohP3l_C376oDZTF50dlTshMJPlO-vfL6_dWPPlPMEBl364AcwIcsQX_O1HPZm22iWc1ytfBZUcX_Xq3ZJq1PKNyKiV7Tts1wi0iNEuK3UGmKJjLhfBsVhWwbF_e687O0hFyqm6WjGV90Im2isRsU968A47WsBxnjpkHAb61GJZwuBg6YFDBJW5aUVPG43gEmfSyZz7UNgPAqnafOzPxp4ZefISRJueDsWay2cofhxljTZdzVZtsw8Xb9frcJ5fBtffnX2OYQ9XBJnw8ZOfAawT5ggm4t6cxnRPSsNFVCckZWRZ7wGzlHrMJzUp69JegglIsghGMZB3Zs72V95wh3K6Pp1peFSMdeuxNrKwDVdUeNz3V6kG013Cz18OnY6agrHrnMw6mJE7iUTRvQiMxYujBYeWJii7vFhH1_1ZYjfX64ML40ZddzRY6G6qnWUTkghbluh9EaPyEEkzJdAyUqV3cZx7_BzY_XBtojkWVhh1dMXtkBsOpA2cwGkicqf80lCly_f__OV1k2jbaInZvEgRE17FFgwV6XGo_IpYjyWY96a77U3B8UWC88KkARWDaH_4Ho2Mc3uyX82-v400i76vxgPTkFhw0IAvdb3DHZ0hOQFbEU0C2ugGaPZgc5AHIcrx2I_YVfwFJtr7sZMiBzU4muwUI-Z-ow9BRLGDtJ2_oJOlTCh_oLiNfPPMP2hPxgXb8scAQ2cm1mEui9eNEYp6A4nL0CKnchdjtKV4VRnu9iBGiP3Pbl4gy23dHHH3uLW_rBzZgfTEJkaxZMTWdJUMGH82PlSeT-KdUEhfWQyF0_azy8W7k76Z4flkUHiGDqKb7LN4JVhumCC7Zda9hjpFxsCdGOtsjY1qwWR7f5D3PyBIq8-XIz_YgvNdsOnnUngakJJITQn7LeTFA0OGR58sfgAAqxcR-0t-uSovfGLPDOVZH0oCb-DfHVqzc3hQd8ifypqh8uJTWMfza-A6lYJomfhxvCv8IqZDhmYnD2Eg0tHSrWLg25pEswXWfjsDZTaOp7k0_mEYGnKqTKU_X5D0sxNGXawUWeb0GQNMA8tGLEO3sdRlbyQCddq2tMgoXzWA2rVZXo_zpgCOiJChGifDovMB3sMwW7vupDUqzLiC-QpBn4l1CTGy62nFpKnt4bp5Po_EkltM01N7MSPeHCDDNOqtBLfQ_7YVp7yBmZt18GfCz-5_UWzoZgBnNdEp7NrlAL8RBO5f1Bmfk93LKD8RZT60_4SN5fj_IXD09ramacxy_-htmNDjXiSBA7c8MfGq9kXMC3HhNP-bhsuMLIR0s6rHio7Jysyv5x1lpEosJOT9Bj7A3d8KsVXqu6YCXmWJC6noN6jQHNnsbqKQTGgi_TkZKif0MMijoB58E2uELAU-WnrL2buwk5FIm9PFZAPqregc-w6BE07lV6SUq84dJ7hvQIrueS1gSnnL04nllbmOdZbdLuxqTLzCwrFebgzSCsQCyyNYh1oQz-5eUvL0vIg-gM6YyybqP5LWwUnPNkp2TZqOHIiDIIlcRdpYCika6-4WF71gDTaUWpywbryDCzLAVoBb_IkPnbqf2Mj5XNTdO4zpIsHKnow9Cb9agsdO8qnVzWVugxxVRUwta0QnVVfM99ZFkC3rP0GvtJI5WNBHK5QmTySFP0ofWM2tjZuSqsmVsPtp3ctBvrx3oApam5bm27gsNE14plYqIY1zqCsrTQAq3LzP-EKIGnvbABmFnAk5C4iUA7tkrjVE5UwWaGfghYqwlXuX0exs3Dr9LWsDYSRwFH2GU79PYwVAYPGKyT1FENgfRc6x4jYNmwDeI0wpPYRRM-DE_xyxLv7EQutR05jyBLUVlq1RztkfVeKdp0YYcNQczJ9jPBH5SV0NBFaxKE1p7y0jPU8BBERL_VEYCOQ9ibMzPf1Gu0_wtzA7oZR0Lgp3hFrBI1V4cA1mSDHF3jvj9kWVpJatC6ITBTNqS6EYISnIa0N5RZC6dsgbtCWg4xPzDvV_HontIWkGlOT9_tmE0lyyt1qptZ_8XzJvrzgSnS1GgIF2ENkQkkLxr_htYHrsZ8RMWu3exTmXygWPyeYNkOnhD4VnTad7T8qJ8LHJEiS0o_OiGnGuub8Li2zDORgLqFjk4Peilmg8BrKA95gy0-eJhxQCVtZ2AunCR8o447HG9GrRdZOCAsPpuFFDcvVlW-mqbzleTrgoMhfd_JY7uEs9puSXLPPZfpidxOUyu2yMMi2Mw8o52Zy251xAfj-OkGuv2CbNK3nazaS24YUFUQyztgZdeV-lhvPBhpYQvcj0vcA-d_W6tkuO_sZNLqwXQzvbV1N1bxl7FS27xkh0Coao6V_spJR5v7hGa1Q-fyKwIiTRp15BEWtzpFQ3aAaaRCOWVoJN5i98utYxBcMqh6iXtb6Q0d2IRx2E5oyS-Vbq3oHZ6UIjBF2sl6Re1DwnNdTcJUhX7ZRqRX3_oCYOlJrKdaKHp5araf8M2Z2Iwd1L7lC2qt4dgBFLLHg63-vE_eMhquHr7ypdvd7B6naHWzNopQ2-gZvZ9xjzksJTuMNrbfOgMq4AiTG1S_dkuXZ-V-oh5UmkLmee1Q3P2XhP4nQiXGwoc4oA_4F2GK-KOyQ-rP8cy_zGLoK9bXCknvwQymqpr0iSds30ZjiCi7SUTCoG-Ai7HSuoIgLeL4Gux9zLFeFMaUoIrKuUvulp9JMRw1ASErXphziZTvzQLtEuDPHvK01wE7MNTfoXQV7csc1vDJAbgaXLHeCJIIDx9jcqt3E1OvvH9W8TDe5E6J4s-LhUU7H5nZbaB7HUyN779CCRNhvtpsku8RkM2O5XZYC1kw1CgMLWYPkRMZdki7pdeWh1SDMZoB1UTuCfWJljtvpYetsidgveFTq3grQ0MoRqJAEziz_widKOHFKGThuXz5PNvXP1i0shotuUhz1V5yMmI302O7ZJsw8wkiQ1dVSWcy8cYb8ogrSiBq0XLZZCUTMy7gIwqUowNiIx9roYOmYZ4ySwSWQqBxuhdPj46aLM8_B8dXFifzqtsH-WTcd29crCWJzNwUX_BaUvkH3TZfKm6WQYUwQm2CUb_s0EyflnxEji2SqAxeBNXx3rFmctJWS-g-3nt4YE10RBAycBBXYaVtcKZS0Sr_Rmj7A3Wz2Cjy6X3t6mctmZ6scaKD5CeI_D8jmZCmWaCKKLigz3xVDMXtNxBmr6dBmWi63j7lVrE3T0FYvEHWD_-4-JJ8Q==","legacy_responseItem_reasoning_61",[769,770],{"_45":772,"_90":700},{"_45":771,"_90":700},"**Crafting final PR description with citations**\n\nI'm preparing the complete PR body with a proposed title, structured into Summary, Why, and How to Test sections, fully citing relevant files like model factory, evals, costs.json, and README. This will clarify all updates including new modules, openrouter support, updated pricing, debug features, scripts, and pinned dependencies. I'll focus on providing clear references for verification commands based on available docs.","**Drafting detailed PR description**\n\nI'm outlining a PR titled for late 2025 updates covering new model factory tools, openrouter integration with config/env updates, refreshed model pricing in costs.json, enhanced mermaid evals with retries and debug traces, added scripts for merging benchmarks and verifying retries, plus pinning mermaid CLI version. The description will include change summaries, motivations centered on multi-provider support and improved debugging, output schema impacts like new debug trace files and usage columns, and clear instructions referencing README and scripts for running and verification.",{"_670":774,"_662":663,"_672":81},{"_673":775,"_41":776,"_694":777,"_90":696},[771],"legacy_reasoning_60",[],{"_670":779,"_662":663,"_672":81},{"_673":780,"_41":776,"_694":781,"_90":696},[],[],{"_670":783,"_662":663,"_672":81},{"_673":784,"_41":785,"_694":786,"_90":696},[772],"legacy_reasoning_59",[],{"_670":788,"_662":663,"_672":81},{"_673":789,"_41":785,"_694":790,"_90":696},[],[],{"_670":792,"_662":663,"_672":81},{"_722":793,"_724":794,"_90":726},"call_o6T8FfgmUq0mljNsJCtLlAiT",{"_408":795,"_728":-5},"Chunk ID: fca1be\nWall time: 0.0504 seconds\nProcess exited with code 0\nOriginal token count: 1443\nOutput:\n 1\t#!/usr/bin/env python3\r\n 2\t\"\"\"\r\n 3\tOffline verification for run-level retry + backoff orchestration.\r\n 4\t\r\n 5\tThis script uses a stub Dataset to simulate:\r\n 6\t- evaluation timeouts (retryable at run-level)\r\n 7\t- report-based retry triggers (e.g. http_error_429, error_ExceptionGroup)\r\n 8\t- non-retry conditions (usage_limit_exceeded, agent_timeout)\r\n 9\t- fatal conditions (Mermaid validator unavailable)\r\n 10\t\r\n 11\tIt patches delays to zero so it runs quickly and deterministically.\r\n 12\t\"\"\"\r\n 13\t\r\n 14\tfrom __future__ import annotations\r\n 15\t\r\n 16\timport asyncio\r\n 17\tfrom dataclasses import dataclass, field\r\n 18\tfrom typing import Any, List, Sequence\r\n 19\t\r\n 20\timport agents_mcp_usage.evaluations.mermaid_evals.run_multi_evals as run_multi_evals\r\n 21\tfrom agents_mcp_usage.evaluations.mermaid_evals.evals_pydantic_mcp import (\r\n 22\t MermaidValidatorUnavailableError,\r\n 23\t)\r\n 24\tfrom agents_mcp_usage.evaluations.mermaid_evals.run_multi_evals import MultiModelEvaluator\r\n 25\t\r\n 26\t\r\n 27\t@dataclass(slots=True)\r\n 28\tclass StubOutput:\r\n 29\t failure_reason: str = \"\"\r\n 30\t fixed_diagram: str = \"\"\r\n 31\t metrics: dict[str, Any] = field(default_factory=dict)\r\n 32\t tools_used: list[str] = field(default_factory=list)\r\n 33\t\r\n 34\t\r\n 35\t@dataclass(slots=True)\r\n 36\tclass StubCase:\r\n 37\t name: str\r\n 38\t task_duration: float = 0.0\r\n 39\t output: StubOutput = field(default_factory=StubOutput)\r\n 40\t scores: dict[str, Any] = field(default_factory=dict)\r\n 41\t\r\n 42\t\r\n 43\t@dataclass(slots=True)\r\n 44\tclass StubReport:\r\n 45\t cases: list[StubCase]\r\n 46\t\r\n 47\t\r\n 48\tclass StubDataset:\r\n 49\t \"\"\"A minimal stub that matches the Dataset.evaluate() shape we need.\"\"\"\r\n 50\t\r\n 51\t def __init__(self, sequence: Sequence[object]) -> None:\r\n 52\t self.sequence = list(sequence)\r\n 53\t self.calls = 0\r\n 54\t\r\n 55\t async def evaluate(self, *_args: Any, **_kwargs: Any) -> object:\r\n 56\t item = self.sequence[self.calls]\r\n 57\t self.calls += 1\r\n 58\t if isinstance(item, BaseException):\r\n 59\t raise item\r\n 60\t return item\r\n 61\t\r\n 62\t\r\n 63\tasync def _no_sleep(_delay: float) -> None:\r\n 64\t return None\r\n 65\t\r\n 66\t\r\n 67\tdef _make_report(failure_reasons: List[str]) -> StubReport:\r\n 68\t cases = [\r\n 69\t StubCase(name=f\"case_{idx}\", output=StubOutput(failure_reason=reason))\r\n 70\t for idx, reason in enumerate(failure_reasons)\r\n 71\t ]\r\n 72\t return StubReport(cases=cases)\r\n 73\t\r\n 74\t\r\n 75\tasync def main() -> None:\r\n 76\t # Patch delay + sleep so retries are fast in this script\r\n 77\t run_multi_evals.compute_exponential_backoff_delay = lambda *_a, **_k: 0.0 # type: ignore[assignment]\r\n 78\t run_multi_evals.asyncio.sleep = _no_sleep # type: ignore[assignment]\r\n 79\t\r\n 80\t model = \"test-model\"\r\n 81\t\r\n 82\t # 1) Evaluation timeout should retry up to MAX_RETRY_ATTEMPTS, then fail.\r\n 83\t evaluator = MultiModelEvaluator(models=[model], judge_model=\"judge\", output_dir=\"./mermaid_eval_results\")\r\n 84\t dataset = StubDataset([asyncio.TimeoutError()] * run_multi_evals.MAX_RETRY_ATTEMPTS)\r\n 85\t result = await evaluator.run_single_evaluation(model, 0, dataset, timeout=0.01)\r\n 86\t assert result is None\r\n 87\t assert evaluator.results[model].failed_runs[0][\"attempts\"] == run_multi_evals.MAX_RETRY_ATTEMPTS\r\n 88\t\r\n 89\t # 2) Retryable report failure_reason should trigger a retry, then succeed.\r\n 90\t evaluator = MultiModelEvaluator(models=[model], judge_model=\"judge\", output_dir=\"./mermaid_eval_results\")\r\n 91\t dataset = StubDataset([_make_report([\"http_error_429\"]), _make_report([\"\"])])\r\n 92\t result = await evaluator.run_single_evaluation(model, 0, dataset, timeout=1)\r\n 93\t assert result is not None\r\n 94\t assert result.attempts == 2\r\n 95\t assert dataset.calls == 2\r\n 96\t\r\n 97\t # 3) usage_limit_exceeded should NOT trigger a retry.\r\n 98\t evaluator = MultiModelEvaluator(models=[model], judge_model=\"judge\", output_dir=\"./mermaid_eval_results\")\r\n 99\t dataset = StubDataset([_make_report([\"usage_limit_exceeded\"]), _make_report([\"\"])])\r\n 100\t result = await evaluator.run_single_evaluation(model, 0, dataset, timeout=1)\r\n 101\t assert result is not None\r\n 102\t assert result.attempts == 1\r\n 103\t assert dataset.calls == 1\r\n 104\t\r\n 105\t # 4) agent_timeout should NOT trigger a retry.\r\n 106\t evaluator = MultiModelEvaluator(models=[model], judge_model=\"judge\", output_dir=\"./mermaid_eval_results\")\r\n 107\t dataset = StubDataset([_make_report([\"agent_timeout\"]), _make_report([\"\"])])\r\n 108\t result = await evaluator.run_single_evaluation(model, 0, dataset, timeout=1)\r\n 109\t assert result is not None\r\n 110\t assert result.attempts == 1\r\n 111\t assert dataset.calls == 1\r\n 112\t\r\n 113\t # 5) Mermaid validator unavailability should fail-fast (raise).\r\n 114\t evaluator = MultiModelEvaluator(models=[model], judge_model=\"judge\", output_dir=\"./mermaid_eval_results\")\r\n 115\t dataset = StubDataset([MermaidValidatorUnavailableError(\"validator down\")])\r\n 116\t try:\r\n 117\t await evaluator.run_single_evaluation(model, 0, dataset, timeout=1)\r\n 118\t raise AssertionError(\"Expected MermaidValidatorUnavailableError\")\r\n 119\t except MermaidValidatorUnavailableError:\r\n 120\t pass\r\n 121\t\r\n 122\t print(\"verify_retry_orchestration.py: OK\")\r\n 123\t\r\n 124\t\r\n 125\tif __name__ == \"__main__\":\r\n 126\t asyncio.run(main())\r\n",{"_670":797,"_662":663,"_672":81},{"_731":798,"_733":799,"_735":800,"_737":738,"_739":801,"_741":31,"_41":793,"_742":802,"_744":745,"_667":111,"_90":746}," 1\t#!/usr/bin/env python3\r\n 2\t\"\"\"\r\n 3\tOffline verification for run-level retry + backoff orchestration.\r\n 4\t\r\n 5\tThis script uses a stub Dataset to simulate:\r\n 6\t- evaluation timeouts (retryable at run-level)\r\n 7\t- report-based retry triggers (e.g. http_error_429, error_ExceptionGroup)\r\n 8\t- non-retry conditions (usage_limit_exceeded, agent_timeout)\r\n 9\t- fatal conditions (Mermaid validator unavailable)\r\n 10\t\r\n 11\tIt patches delays to zero so it runs quickly and deterministically.\r\n 12\t\"\"\"\r\n 13\t\r\n 14\tfrom __future__ import annotations\r\n 15\t\r\n 16\timport asyncio\r\n 17\tfrom dataclasses import dataclass, field\r\n 18\tfrom typing import Any, List, Sequence\r\n 19\t\r\n 20\timport agents_mcp_usage.evaluations.mermaid_evals.run_multi_evals as run_multi_evals\r\n 21\tfrom agents_mcp_usage.evaluations.mermaid_evals.evals_pydantic_mcp import (\r\n 22\t MermaidValidatorUnavailableError,\r\n 23\t)\r\n 24\tfrom agents_mcp_usage.evaluations.mermaid_evals.run_multi_evals import MultiModelEvaluator\r\n 25\t\r\n 26\t\r\n 27\t@dataclass(slots=True)\r\n 28\tclass StubOutput:\r\n 29\t failure_reason: str = \"\"\r\n 30\t fixed_diagram: str = \"\"\r\n 31\t metrics: dict[str, Any] = field(default_factory=dict)\r\n 32\t tools_used: list[str] = field(default_factory=list)\r\n 33\t\r\n 34\t\r\n 35\t@dataclass(slots=True)\r\n 36\tclass StubCase:\r\n 37\t name: str\r\n 38\t task_duration: float = 0.0\r\n 39\t output: StubOutput = field(default_factory=StubOutput)\r\n 40\t scores: dict[str, Any] = field(default_factory=dict)\r\n 41\t\r\n 42\t\r\n 43\t@dataclass(slots=True)\r\n 44\tclass StubReport:\r\n 45\t cases: list[StubCase]\r\n 46\t\r\n 47\t\r\n 48\tclass StubDataset:\r\n 49\t \"\"\"A minimal stub that matches the Dataset.evaluate() shape we need.\"\"\"\r\n 50\t\r\n 51\t def __init__(self, sequence: Sequence[object]) -> None:\r\n 52\t self.sequence = list(sequence)\r\n 53\t self.calls = 0\r\n 54\t\r\n 55\t async def evaluate(self, *_args: Any, **_kwargs: Any) -> object:\r\n 56\t item = self.sequence[self.calls]\r\n 57\t self.calls += 1\r\n 58\t if isinstance(item, BaseException):\r\n 59\t raise item\r\n 60\t return item\r\n 61\t\r\n 62\t\r\n 63\tasync def _no_sleep(_delay: float) -> None:\r\n 64\t return None\r\n 65\t\r\n 66\t\r\n 67\tdef _make_report(failure_reasons: List[str]) -> StubReport:\r\n 68\t cases = [\r\n 69\t StubCase(name=f\"case_{idx}\", output=StubOutput(failure_reason=reason))\r\n 70\t for idx, reason in enumerate(failure_reasons)\r\n 71\t ]\r\n 72\t return StubReport(cases=cases)\r\n 73\t\r\n 74\t\r\n 75\tasync def main() -> None:\r\n 76\t # Patch delay + sleep so retries are fast in this script\r\n 77\t run_multi_evals.compute_exponential_backoff_delay = lambda *_a, **_k: 0.0 # type: ignore[assignment]\r\n 78\t run_multi_evals.asyncio.sleep = _no_sleep # type: ignore[assignment]\r\n 79\t\r\n 80\t model = \"test-model\"\r\n 81\t\r\n 82\t # 1) Evaluation timeout should retry up to MAX_RETRY_ATTEMPTS, then fail.\r\n 83\t evaluator = MultiModelEvaluator(models=[model], judge_model=\"judge\", output_dir=\"./mermaid_eval_results\")\r\n 84\t dataset = StubDataset([asyncio.TimeoutError()] * run_multi_evals.MAX_RETRY_ATTEMPTS)\r\n 85\t result = await evaluator.run_single_evaluation(model, 0, dataset, timeout=0.01)\r\n 86\t assert result is None\r\n 87\t assert evaluator.results[model].failed_runs[0][\"attempts\"] == run_multi_evals.MAX_RETRY_ATTEMPTS\r\n 88\t\r\n 89\t # 2) Retryable report failure_reason should trigger a retry, then succeed.\r\n 90\t evaluator = MultiModelEvaluator(models=[model], judge_model=\"judge\", output_dir=\"./mermaid_eval_results\")\r\n 91\t dataset = StubDataset([_make_report([\"http_error_429\"]), _make_report([\"\"])])\r\n 92\t result = await evaluator.run_single_evaluation(model, 0, dataset, timeout=1)\r\n 93\t assert result is not None\r\n 94\t assert result.attempts == 2\r\n 95\t assert dataset.calls == 2\r\n 96\t\r\n 97\t # 3) usage_limit_exceeded should NOT trigger a retry.\r\n 98\t evaluator = MultiModelEvaluator(models=[model], judge_model=\"judge\", output_dir=\"./mermaid_eval_results\")\r\n 99\t dataset = StubDataset([_make_report([\"usage_limit_exceeded\"]), _make_report([\"\"])])\r\n 100\t result = await evaluator.run_single_evaluation(model, 0, dataset, timeout=1)\r\n 101\t assert result is not None\r\n 102\t assert result.attempts == 1\r\n 103\t assert dataset.calls == 1\r\n 104\t\r\n 105\t # 4) agent_timeout should NOT trigger a retry.\r\n 106\t evaluator = MultiModelEvaluator(models=[model], judge_model=\"judge\", output_dir=\"./mermaid_eval_results\")\r\n 107\t dataset = StubDataset([_make_report([\"agent_timeout\"]), _make_report([\"\"])])\r\n 108\t result = await evaluator.run_single_evaluation(model, 0, dataset, timeout=1)\r\n 109\t assert result is not None\r\n 110\t assert result.attempts == 1\r\n 111\t assert dataset.calls == 1\r\n 112\t\r\n 113\t # 5) Mermaid validator unavailability should fail-fast (raise).\r\n 114\t evaluator = MultiModelEvaluator(models=[model], judge_model=\"judge\", output_dir=\"./mermaid_eval_results\")\r\n 115\t dataset = StubDataset([MermaidValidatorUnavailableError(\"validator down\")])\r\n 116\t try:\r\n 117\t await evaluator.run_single_evaluation(model, 0, dataset, timeout=1)\r\n 118\t raise AssertionError(\"Expected MermaidValidatorUnavailableError\")\r\n 119\t except MermaidValidatorUnavailableError:\r\n 120\t pass\r\n 121\t\r\n 122\t print(\"verify_retry_orchestration.py: OK\")\r\n 123\t\r\n 124\t\r\n 125\tif __name__ == \"__main__\":\r\n 126\t asyncio.run(main())\r\n","/bin/bash -lc 'nl -ba agents_mcp_usage/evaluations/mermaid_evals/scripts/verify_retry_orchestration.py | sed -n '\"'\"'1,200p'\"'\"''",[803],50,"4153",{"_733":804,"_297":805,"_216":806,"_90":751},"nl -ba agents_mcp_usage/evaluations/mermaid_evals/scripts/verify_retry_orchestration.py | sed -n '1,200p'","verify_retry_orchestration.py","agents_mcp_usage/evaluations/mermaid_evals/scripts/verify_retry_orchestration.py",{"_670":808,"_662":663,"_672":81},{"_731":-5,"_733":799,"_735":809,"_737":738,"_739":-5,"_741":-5,"_41":793,"_742":802,"_744":745,"_667":755,"_90":746},[810],{"_733":804,"_297":805,"_216":806,"_90":751},{"_670":812,"_662":663,"_672":81},{"_759":813,"_722":793,"_41":-5,"_297":761,"_762":-5,"_90":763},"{\"cmd\":\"nl -ba agents_mcp_usage/evaluations/mermaid_evals/scripts/verify_retry_orchestration.py | sed -n '1,200p'\"}",{"_670":815,"_662":663,"_672":81},{"_673":-5,"_691":816,"_41":817,"_694":818,"_90":696},"gAAAAABpaAj6ZCB5CShW_T_Ap2anx-Ax-kgL3X3u4YucJIlFCmxQnOu4Cak6AEg7llUsvS8XXZ2QYGW6rXyksiDWdbr_MYIRnq6RWmql1XCG_2B2xajaJF4AcMv1YbnTIvhR-Ffu-CU0FOQmLS1LYMN_Hr2nXQe9HKrjOz_cemanfhL5cDgSA-unQDbE4s7jrrgsXSLcaSPMSloBpa-R2xbVXomAGzAUz1ydTNFhcFIk-q4G6ZqdshNEaw4lKM-ADn2lTliA0nNKbpuVWs4Vg2yAMktSGNEi0tPg8RyrmHVG71RZpp4z628jlbH8q1e9MYkimwawXkRPcb050V_oHG-vLQkR8tBG3gu_Quq2SXkV_TtZmYhO2EfTqb3hk-hclU5cqseHSi74wf6_03TUQ7wP0mY8eqX4EdoQ5iqlx1HmhGuQIXEev0pPckgz6ma6H0K5IuPVhHSnqXK5yr_t8zlFdTQgbtn9ztELLBTOxL68IOetQGTRS2fP4PrCjzWXyrxWJ3mDcmNvy0jTRhscMfuY6VK-4tK4NvS7thpiavKtrGmHnSILeiDfn2QBr1kdf6LH3v9YE0WdBajn32Fs4B75RRADgmVqbqc8LyObzj8FFQr-I98IqdRDl_gh_PryqBCSgeZ_bsfili_R1DuYVbw1kfhDNlZ2BIO9XI-PQgI47eV3k7zGKXhOlmVFXJwQdOhaXdJpG9TGDAFPfeFUqlmNlqsJZCfPbEgrCFXQole6bm77qKA4PIiTr5jGlPBxB4AXiZTOK8YyX8V6f7oe498YufSifdOZQefEsbbfGNZqF8cLsqpym1stlPRyHsEYm7PyEhdnQuWE5lgETTdV1FskpovHSQNDf351B-RztqUQr3jRie8pXLt4YLOtV-gDdfm10XbBOhAWZP2coZvypi6Ge3lhGZaKea3XRHJlk6-SiE9XqqzcVuLaPjJJcFj3ldOe9qu71EVHuLUdgxZmwcvhota-2Jhhc7jI0RGBk34EzgIl1PPvJaRn0C9NPJ2OxvCPMkqZc9kNoG3awbnc5TgyPPq3ugNPSxHGFTz9U040W7SXb6nXhI0=","legacy_responseItem_reasoning_58",[819],{"_45":820,"_90":700},"**Noting README and script update considerations**\n\nI'm considering whether to update the README for basic_mcp and the main README, and adding a citation for the new offline retry verification script named verify_retry_orchestration.",{"_670":822,"_662":663,"_672":81},{"_673":823,"_41":824,"_694":825,"_90":696},[820],"legacy_reasoning_57",[],{"_670":827,"_662":663,"_672":81},{"_673":828,"_41":824,"_694":829,"_90":696},[],[],{"_670":831,"_662":663,"_672":81},{"_722":832,"_724":833,"_90":726},"call_EKT5idPeXvF1fMsNhlYAaFUe",{"_408":834,"_728":-5},"Chunk ID: d14b47\nWall time: 0.0517 seconds\nProcess exited with code 0\nOriginal token count: 2018\nOutput:\n 1\t# Model Factory Documentation\r\n 2\t\r\n 3\tThe model factory pattern provides a centralized way to create models across different providers in PydanticAI, leveraging the framework's built-in provider support while handling provider-specific configurations.\r\n 4\t\r\n 5\t## Overview\r\n 6\t\r\n 7\tThe model factory (`agents_mcp_usage/factory/model_factory.py`) replaces manual string parsing with a configuration-driven approach that:\r\n 8\t\r\n 9\t- Supports 13+ providers out of the box\r\n 10\t- Leverages PydanticAI's native provider detection\r\n 11\t- Handles provider-specific quirks via model profiles\r\n 12\t- Provides an extensible design for adding new providers\r\n 13\t\r\n 14\t## Usage\r\n 15\t\r\n 16\t### Basic Usage\r\n 17\t\r\n 18\t```python\r\n 19\tfrom agents_mcp_usage.factory.model_factory import create_agent\r\n 20\t\r\n 21\t# Using provider:model format\r\n 22\tagent = create_agent(\r\n 23\t model=\"deepseek:deepseek-chat\",\r\n 24\t mcp_servers=get_mcp_servers(),\r\n 25\t model_settings={\"temperature\": 0.7}\r\n 26\t)\r\n 27\t\r\n 28\t# Using native model names (automatically detected)\r\n 29\tagent = create_agent(\r\n 30\t model=\"gemini-2.5-pro\", # Google provider auto-detected\r\n 31\t mcp_servers=get_mcp_servers()\r\n 32\t)\r\n 33\t```\r\n 34\t\r\n 35\t### Model String Formats\r\n 36\t\r\n 37\tThe factory supports multiple model string formats:\r\n 38\t\r\n 39\t1. **Provider Prefix**: `provider:model`\r\n 40\t - `deepseek:deepseek-chat`\r\n 41\t - `openrouter:google/gemini-2.5-pro`\r\n 42\t - `github:xai/grok-3-mini`\r\n 43\t\r\n 44\t2. **Native Models**: Direct names (provider auto-detected)\r\n 45\t - `gemini-2.5-pro` → Google\r\n 46\t - `gpt-4` → OpenAI\r\n 47\t - `claude-3-opus` → Anthropic\r\n 48\t\r\n 49\t3. **Special Formats**:\r\n 50\t - Bedrock: `bedrock:us.amazon.nova-pro-v1:0`\r\n 51\t - Ollama: `ollama:llama3.2`\r\n 52\t\r\n 53\t## Supported Providers\r\n 54\t\r\n 55\t### Native PydanticAI Providers\r\n 56\tThese providers are handled directly by PydanticAI without special configuration:\r\n 57\t- **Google**: Gemini models\r\n 58\t- **OpenAI**: GPT models\r\n 59\t- **Anthropic**: Claude models\r\n 60\t\r\n 61\t### OpenAI-Compatible Providers\r\n 62\tThese providers use the OpenAI API format with custom endpoints:\r\n 63\t\r\n 64\t| Provider | Env Variable | Example Model |\r\n 65\t|----------|-------------|---------------|\r\n 66\t| DeepSeek | `DEEPSEEK_API_KEY` | `deepseek:deepseek-chat` |\r\n 67\t| OpenRouter | `OPENROUTER_API_KEY` | `openrouter:anthropic/claude-3.5-sonnet` |\r\n 68\t| Perplexity | `PERPLEXITY_API_KEY` | `perplexity:sonar-pro` |\r\n 69\t| Ollama | None (local) | `ollama:llama3.2` |\r\n 70\t\r\n 71\t### OpenRouter (explicit provider handling)\r\n 72\t\r\n 73\tThis repo intentionally constructs an explicit OpenRouter provider/model for `openrouter:*` model strings, rather than relying on PydanticAI's implicit provider detection.\r\n 74\t\r\n 75\tImplementation details:\r\n 76\t- `openrouter:/` is parsed by [`parse_model_string()`](agents_mcp_usage/factory/model_factory.py:110) and handled in [`create_model()`](agents_mcp_usage/factory/model_factory.py:258) via the OpenRouter handler.\r\n 77\t- The handler builds an OpenAI-compatible client pointed at `https://openrouter.ai/api/v1` and wraps it in PydanticAI's [`OpenRouterProvider`](agents_mcp_usage/factory/model_factory.py:222).\r\n 78\t\r\n 79\t#### App attribution (optional)\r\n 80\t\r\n 81\tOpenRouter supports attribution via headers.\r\n 82\t\r\n 83\tSet these environment variables (optional):\r\n 84\t\r\n 85\t```bash\r\n 86\texport OPENROUTER_APP_URL=\"https://your-app.com\" # sent as HTTP-Referer\r\n 87\texport OPENROUTER_APP_TITLE=\"Your App\" # sent as X-Title\r\n 88\t```\r\n 89\t\r\n 90\tOr pass overrides via `provider_kwargs`:\r\n 91\t\r\n 92\t```python\r\n 93\tagent = create_agent(\r\n 94\t model=\"openrouter:anthropic/claude-3.5-sonnet\",\r\n 95\t provider_kwargs={\r\n 96\t \"api_key\": \"...\",\r\n 97\t \"app_url\": \"https://your-app.com\",\r\n 98\t \"app_title\": \"Your App\",\r\n 99\t },\r\n 100\t)\r\n 101\t```\r\n 102\t\r\n 103\t### Cloud Providers\r\n 104\t\r\n 105\t| Provider | Configuration | Example Model |\r\n 106\t|----------|--------------|---------------|\r\n 107\t| AWS Bedrock | AWS credentials | `bedrock:us.amazon.nova-pro-v1:0` |\r\n 108\t| Azure AI | Endpoint + API key | `azure:gpt-4` |\r\n 109\t| Heroku AI | `HEROKU_INFERENCE_KEY` | `heroku:claude-3-sonnet` |\r\n 110\t\r\n 111\t### Specialized Providers\r\n 112\t\r\n 113\t| Provider | Env Variable | Example Model |\r\n 114\t|----------|-------------|---------------|\r\n 115\t| GitHub Models | `GITHUB_API_KEY` | `github:xai/grok-3-mini` |\r\n 116\t| Grok (xAI) | `XAI_API_KEY` | `grok:grok-2-1212` |\r\n 117\t| Fireworks | `FIREWORKS_API_KEY` | `fireworks:accounts/fireworks/models/qwq-32b` |\r\n 118\t| Together | `TOGETHER_API_KEY` | `together:meta-llama/Llama-3.3-70B` |\r\n 119\t\r\n 120\t## Model Profiles\r\n 121\t\r\n 122\tModel profiles handle provider-specific behaviors and limitations:\r\n 123\t\r\n 124\t```python\r\n 125\tprofile = OpenAIModelProfile(\r\n 126\t # Some providers don't support JSON schema references\r\n 127\t json_schema_transformer=InlineDefsJsonSchemaTransformer,\r\n 128\t \r\n 129\t # Some providers don't support strict tool definitions\r\n 130\t openai_supports_strict_tool_definition=False\r\n 131\t)\r\n 132\t```\r\n 133\t\r\n 134\t### Providers Requiring Profiles\r\n 135\t- DeepSeek: Requires inline JSON schemas\r\n 136\t- Ollama: Limited tool definition support\r\n 137\t\r\n 138\t## Adding New Providers\r\n 139\t\r\n 140\tTo add a new provider, update the `PROVIDER_CONFIGS` dictionary:\r\n 141\t\r\n 142\t```python\r\n 143\tPROVIDER_CONFIGS = {\r\n 144\t \"new_provider\": {\r\n 145\t \"provider_class\": \"pydantic_ai.providers.new_provider.NewProvider\",\r\n 146\t \"env_var\": \"NEW_PROVIDER_API_KEY\",\r\n 147\t \"base_url\": \"https://api.newprovider.com\", # Optional\r\n 148\t \"profile\": { # Optional\r\n 149\t \"json_schema_transformer\": InlineDefsJsonSchemaTransformer,\r\n 150\t \"openai_supports_strict_tool_definition\": False\r\n 151\t }\r\n 152\t }\r\n 153\t}\r\n 154\t```\r\n 155\t\r\n 156\t## Architecture\r\n 157\t\r\n 158\t### Components\r\n 159\t\r\n 160\t1. **parse_model_string()**: Extracts provider and model name from string\r\n 161\t2. **create_model()**: Creates appropriate model instance or returns string for PydanticAI\r\n 162\t3. **create_agent()**: Convenience function to create agents with proper model handling\r\n 163\t4. **PROVIDER_CONFIGS**: Central configuration for all providers\r\n 164\t\r\n 165\t### Flow\r\n 166\t\r\n 167\t1. Model string is parsed to identify provider\r\n 168\t2. If native provider (Google/OpenAI/Anthropic), return string for PydanticAI\r\n 169\t3. If configured provider, create appropriate model instance\r\n 170\t4. If unknown provider, return string and let PydanticAI handle it\r\n 171\t5. Apply model profiles if specified in configuration\r\n 172\t\r\n 173\t## Benefits\r\n 174\t\r\n 175\t1. **Centralized Configuration**: All provider settings in one place\r\n 176\t2. **Extensibility**: Easy to add new providers\r\n 177\t3. **Consistency**: Same interface regardless of provider\r\n 178\t4. **Flexibility**: Supports both native and custom providers\r\n 179\t5. **Type Safety**: Leverages PydanticAI's type system\r\n 180\t\r\n 181\t## Example: Multi-Provider Comparison\r\n 182\t\r\n 183\t```python\r\n 184\tfrom agents_mcp_usage.factory.model_factory import create_agent\r\n 185\t\r\n 186\tmodels = [\r\n 187\t \"gemini-2.5-pro\",\r\n 188\t \"openai:gpt-4\",\r\n 189\t \"deepseek:deepseek-chat\",\r\n 190\t \"ollama:llama3.2\"\r\n 191\t]\r\n 192\t\r\n 193\tfor model in models:\r\n 194\t agent = create_agent(\r\n 195\t model=model,\r\n 196\t mcp_servers=get_mcp_servers()\r\n 197\t )\r\n 198\t result = await agent.run(\"Solve this problem...\")\r\n 199\t print(f\"{model}: {result}\")\r\n 200\t```\r\n",{"_670":836,"_662":663,"_672":81},{"_731":837,"_733":838,"_735":839,"_737":738,"_739":740,"_741":31,"_41":832,"_742":840,"_744":745,"_667":111,"_90":746}," 1\t# Model Factory Documentation\r\n 2\t\r\n 3\tThe model factory pattern provides a centralized way to create models across different providers in PydanticAI, leveraging the framework's built-in provider support while handling provider-specific configurations.\r\n 4\t\r\n 5\t## Overview\r\n 6\t\r\n 7\tThe model factory (`agents_mcp_usage/factory/model_factory.py`) replaces manual string parsing with a configuration-driven approach that:\r\n 8\t\r\n 9\t- Supports 13+ providers out of the box\r\n 10\t- Leverages PydanticAI's native provider detection\r\n 11\t- Handles provider-specific quirks via model profiles\r\n 12\t- Provides an extensible design for adding new providers\r\n 13\t\r\n 14\t## Usage\r\n 15\t\r\n 16\t### Basic Usage\r\n 17\t\r\n 18\t```python\r\n 19\tfrom agents_mcp_usage.factory.model_factory import create_agent\r\n 20\t\r\n 21\t# Using provider:model format\r\n 22\tagent = create_agent(\r\n 23\t model=\"deepseek:deepseek-chat\",\r\n 24\t mcp_servers=get_mcp_servers(),\r\n 25\t model_settings={\"temperature\": 0.7}\r\n 26\t)\r\n 27\t\r\n 28\t# Using native model names (automatically detected)\r\n 29\tagent = create_agent(\r\n 30\t model=\"gemini-2.5-pro\", # Google provider auto-detected\r\n 31\t mcp_servers=get_mcp_servers()\r\n 32\t)\r\n 33\t```\r\n 34\t\r\n 35\t### Model String Formats\r\n 36\t\r\n 37\tThe factory supports multiple model string formats:\r\n 38\t\r\n 39\t1. **Provider Prefix**: `provider:model`\r\n 40\t - `deepseek:deepseek-chat`\r\n 41\t - `openrouter:google/gemini-2.5-pro`\r\n 42\t - `github:xai/grok-3-mini`\r\n 43\t\r\n 44\t2. **Native Models**: Direct names (provider auto-detected)\r\n 45\t - `gemini-2.5-pro` → Google\r\n 46\t - `gpt-4` → OpenAI\r\n 47\t - `claude-3-opus` → Anthropic\r\n 48\t\r\n 49\t3. **Special Formats**:\r\n 50\t - Bedrock: `bedrock:us.amazon.nova-pro-v1:0`\r\n 51\t - Ollama: `ollama:llama3.2`\r\n 52\t\r\n 53\t## Supported Providers\r\n 54\t\r\n 55\t### Native PydanticAI Providers\r\n 56\tThese providers are handled directly by PydanticAI without special configuration:\r\n 57\t- **Google**: Gemini models\r\n 58\t- **OpenAI**: GPT models\r\n 59\t- **Anthropic**: Claude models\r\n 60\t\r\n 61\t### OpenAI-Compatible Providers\r\n 62\tThese providers use the OpenAI API format with custom endpoints:\r\n 63\t\r\n 64\t| Provider | Env Variable | Example Model |\r\n 65\t|----------|-------------|---------------|\r\n 66\t| DeepSeek | `DEEPSEEK_API_KEY` | `deepseek:deepseek-chat` |\r\n 67\t| OpenRouter | `OPENROUTER_API_KEY` | `openrouter:anthropic/claude-3.5-sonnet` |\r\n 68\t| Perplexity | `PERPLEXITY_API_KEY` | `perplexity:sonar-pro` |\r\n 69\t| Ollama | None (local) | `ollama:llama3.2` |\r\n 70\t\r\n 71\t### OpenRouter (explicit provider handling)\r\n 72\t\r\n 73\tThis repo intentionally constructs an explicit OpenRouter provider/model for `openrouter:*` model strings, rather than relying on PydanticAI's implicit provider detection.\r\n 74\t\r\n 75\tImplementation details:\r\n 76\t- `openrouter:/` is parsed by [`parse_model_string()`](agents_mcp_usage/factory/model_factory.py:110) and handled in [`create_model()`](agents_mcp_usage/factory/model_factory.py:258) via the OpenRouter handler.\r\n 77\t- The handler builds an OpenAI-compatible client pointed at `https://openrouter.ai/api/v1` and wraps it in PydanticAI's [`OpenRouterProvider`](agents_mcp_usage/factory/model_factory.py:222).\r\n 78\t\r\n 79\t#### App attribution (optional)\r\n 80\t\r\n 81\tOpenRouter supports attribution via headers.\r\n 82\t\r\n 83\tSet these environment variables (optional):\r\n 84\t\r\n 85\t```bash\r\n 86\texport OPENROUTER_APP_URL=\"https://your-app.com\" # sent as HTTP-Referer\r\n 87\texport OPENROUTER_APP_TITLE=\"Your App\" # sent as X-Title\r\n 88\t```\r\n 89\t\r\n 90\tOr pass overrides via `provider_kwargs`:\r\n 91\t\r\n 92\t```python\r\n 93\tagent = create_agent(\r\n 94\t model=\"openrouter:anthropic/claude-3.5-sonnet\",\r\n 95\t provider_kwargs={\r\n 96\t \"api_key\": \"...\",\r\n 97\t \"app_url\": \"https://your-app.com\",\r\n 98\t \"app_title\": \"Your App\",\r\n 99\t },\r\n 100\t)\r\n 101\t```\r\n 102\t\r\n 103\t### Cloud Providers\r\n 104\t\r\n 105\t| Provider | Configuration | Example Model |\r\n 106\t|----------|--------------|---------------|\r\n 107\t| AWS Bedrock | AWS credentials | `bedrock:us.amazon.nova-pro-v1:0` |\r\n 108\t| Azure AI | Endpoint + API key | `azure:gpt-4` |\r\n 109\t| Heroku AI | `HEROKU_INFERENCE_KEY` | `heroku:claude-3-sonnet` |\r\n 110\t\r\n 111\t### Specialized Providers\r\n 112\t\r\n 113\t| Provider | Env Variable | Example Model |\r\n 114\t|----------|-------------|---------------|\r\n 115\t| GitHub Models | `GITHUB_API_KEY` | `github:xai/grok-3-mini` |\r\n 116\t| Grok (xAI) | `XAI_API_KEY` | `grok:grok-2-1212` |\r\n 117\t| Fireworks | `FIREWORKS_API_KEY` | `fireworks:accounts/fireworks/models/qwq-32b` |\r\n 118\t| Together | `TOGETHER_API_KEY` | `together:meta-llama/Llama-3.3-70B` |\r\n 119\t\r\n 120\t## Model Profiles\r\n 121\t\r\n 122\tModel profiles handle provider-specific behaviors and limitations:\r\n 123\t\r\n 124\t```python\r\n 125\tprofile = OpenAIModelProfile(\r\n 126\t # Some providers don't support JSON schema references\r\n 127\t json_schema_transformer=InlineDefsJsonSchemaTransformer,\r\n 128\t \r\n 129\t # Some providers don't support strict tool definitions\r\n 130\t openai_supports_strict_tool_definition=False\r\n 131\t)\r\n 132\t```\r\n 133\t\r\n 134\t### Providers Requiring Profiles\r\n 135\t- DeepSeek: Requires inline JSON schemas\r\n 136\t- Ollama: Limited tool definition support\r\n 137\t\r\n 138\t## Adding New Providers\r\n 139\t\r\n 140\tTo add a new provider, update the `PROVIDER_CONFIGS` dictionary:\r\n 141\t\r\n 142\t```python\r\n 143\tPROVIDER_CONFIGS = {\r\n 144\t \"new_provider\": {\r\n 145\t \"provider_class\": \"pydantic_ai.providers.new_provider.NewProvider\",\r\n 146\t \"env_var\": \"NEW_PROVIDER_API_KEY\",\r\n 147\t \"base_url\": \"https://api.newprovider.com\", # Optional\r\n 148\t \"profile\": { # Optional\r\n 149\t \"json_schema_transformer\": InlineDefsJsonSchemaTransformer,\r\n 150\t \"openai_supports_strict_tool_definition\": False\r\n 151\t }\r\n 152\t }\r\n 153\t}\r\n 154\t```\r\n 155\t\r\n 156\t## Architecture\r\n 157\t\r\n 158\t### Components\r\n 159\t\r\n 160\t1. **parse_model_string()**: Extracts provider and model name from string\r\n 161\t2. **create_model()**: Creates appropriate model instance or returns string for PydanticAI\r\n 162\t3. **create_agent()**: Convenience function to create agents with proper model handling\r\n 163\t4. **PROVIDER_CONFIGS**: Central configuration for all providers\r\n 164\t\r\n 165\t### Flow\r\n 166\t\r\n 167\t1. Model string is parsed to identify provider\r\n 168\t2. If native provider (Google/OpenAI/Anthropic), return string for PydanticAI\r\n 169\t3. If configured provider, create appropriate model instance\r\n 170\t4. If unknown provider, return string and let PydanticAI handle it\r\n 171\t5. Apply model profiles if specified in configuration\r\n 172\t\r\n 173\t## Benefits\r\n 174\t\r\n 175\t1. **Centralized Configuration**: All provider settings in one place\r\n 176\t2. **Extensibility**: Easy to add new providers\r\n 177\t3. **Consistency**: Same interface regardless of provider\r\n 178\t4. **Flexibility**: Supports both native and custom providers\r\n 179\t5. **Type Safety**: Leverages PydanticAI's type system\r\n 180\t\r\n 181\t## Example: Multi-Provider Comparison\r\n 182\t\r\n 183\t```python\r\n 184\tfrom agents_mcp_usage.factory.model_factory import create_agent\r\n 185\t\r\n 186\tmodels = [\r\n 187\t \"gemini-2.5-pro\",\r\n 188\t \"openai:gpt-4\",\r\n 189\t \"deepseek:deepseek-chat\",\r\n 190\t \"ollama:llama3.2\"\r\n 191\t]\r\n 192\t\r\n 193\tfor model in models:\r\n 194\t agent = create_agent(\r\n 195\t model=model,\r\n 196\t mcp_servers=get_mcp_servers()\r\n 197\t )\r\n 198\t result = await agent.run(\"Solve this problem...\")\r\n 199\t print(f\"{model}: {result}\")\r\n 200\t```\r\n","/bin/bash -lc 'nl -ba agents_mcp_usage/factory/MODEL_FACTORY.md | sed -n '\"'\"'1,200p'\"'\"''",[841],"10603",{"_733":842,"_297":843,"_216":844,"_90":751},"nl -ba agents_mcp_usage/factory/MODEL_FACTORY.md | sed -n '1,200p'","MODEL_FACTORY.md","agents_mcp_usage/factory/MODEL_FACTORY.md",{"_670":846,"_662":663,"_672":81},{"_731":-5,"_733":838,"_735":847,"_737":738,"_739":-5,"_741":-5,"_41":832,"_742":840,"_744":745,"_667":755,"_90":746},[848],{"_733":842,"_297":843,"_216":844,"_90":751},{"_670":850,"_662":663,"_672":81},{"_759":851,"_722":832,"_41":-5,"_297":761,"_762":-5,"_90":763},"{\"cmd\":\"nl -ba agents_mcp_usage/factory/MODEL_FACTORY.md | sed -n '1,200p'\"}",{"_670":853,"_662":663,"_672":81},{"_673":-5,"_691":854,"_41":855,"_694":856,"_90":696},"gAAAAABpaAj1Oj2S0R5jE2KzGxinuEyV-cSLy_IYAXvmezRVhbsj69ltgGHaFON1M_khJH7NKQ3GMAmRrPRsbW6S2gL7dqQQnWczNlDajZSacFQQCYUfrKhhK8r56qzeU2u0E64coltjByUyOiM71LqCyd0Xynw8PZZ6jC6NvAqMGLZ3G9sIoHOyynGsRimGYPbUv9Y2DgTV-u5UuNnlxse9eBJ-D2QnnY_jqs40tz4VUueuXYqKpfsOLSKygGTdq0Z7_3BoPyrusQFsxcc_nJ9kkjIgvH-vSqwrbq0EnuepsLzyJYziib4lhGMyoZnP-qwhGJmxZvk4p0Cy4b7bl5pI47x-WNGfIzQn8BU6iQ2hoEQB5H9N87JonaWxhxZODzMvMvjdwKfXHLDeyXtFx-YmXAJx6oGIctpfek64RWYSnqGRLMq-SoNtrp21cLaU_7I0_lY3gdJLRcJeKGEGSq_vGQDYBYeiuRl9vgTtRWZ9mUIogBcxG5kV2TMVyRVpLEZtt2HwwnO-uIVPcjRrebNU7RGQIjiFwpmLi1BZ1GRUwJRvEOXTqSE2MpnvCxiu6rhC9_Zt-W6r_O8PgBYPA3DgSc1Yf7YF5CFTiE-xMwReGi_z1UQF9Ceh4ND_pRYZj0BNoVwJwnOV4K1u2FjUnf-F1xR7z0-XvFpcOt909xoibancbidPKc7v6EBf7eB6yEJKGyDIjxvPip5zg1UKYzk7i_FrO-2KtUmcNSbGkSncO1jHICNLl97I5zCKhbHy9Z-DiQgoWvoz0G5pDK3B95tqa5i4ont8CE28smlq87HgSd1VPn9-kKHuKS1cbbdMphY1GQlcPiINsCQg-Ynq9dmFgBNPWpStSU5TNl6w2X5CGw1hxWgj6c-M9z0JsFHHshLRWi-vG4237OhLzYFlkOsszXLqncww6bTQ7e0ANiMa25lzWTcj_vAB0D_AGr_FJlkPgq66SjlSGRcjHPo1bPPvm_g_djCvSJJQsdxJ6zK4R7t6f124K6VQYyyAycn0TwEqeDoV5BmaYBYeIzKWLjkuQnzcIRHZLb4sLTMDq4vGUsKNp3DcsvZksfX3HpVFxpOO5qM1FmV1xpjNaNbGVSRmDvVLymtijQRyGohtcgLktxEGvTwpNR9T8N2s_L9eJ_m33Xe0WkSxKNN4jZeXXWelp42tWqpu-bWybfTaDW7xcA801zZ59xZXqOJxBb_se7KthkboWIy2jFXQeH9-li03GzyZWd6SsA==","legacy_responseItem_reasoning_56",[857],{"_45":858,"_90":700},"**Confirming README and doc references**\n\nThe README already includes the openrouter env line and .env example; next, I'll ensure the new model factory module and its docs are cited properly, using MODEL_FACTORY.md content for reference in the PR description and possibly in basic_mcp.",{"_670":860,"_662":663,"_672":81},{"_673":861,"_41":862,"_694":863,"_90":696},[858],"legacy_reasoning_55",[],{"_670":865,"_662":663,"_672":81},{"_673":866,"_41":862,"_694":867,"_90":696},[],[],{"_670":869,"_662":663,"_672":81},{"_722":870,"_724":871,"_90":726},"call_GyJ3m5cPOo9zMX9I62AHLkJR",{"_408":872,"_728":-5},"Chunk ID: 0b2b8c\nWall time: 0.0513 seconds\nProcess exited with code 0\nOriginal token count: 828\nOutput:\n 1140\t )\r\n 1141\t\r\n 1142\t # Collect all unique evaluator and metric names\r\n 1143\t all_evaluator_names = set()\r\n 1144\t all_metric_names = set()\r\n 1145\t\r\n 1146\t for case in report.cases:\r\n 1147\t all_evaluator_names.update(case.scores.keys())\r\n 1148\t if hasattr(case.output, \"metrics\") and case.output.metrics:\r\n 1149\t all_metric_names.update(case.output.metrics.keys())\r\n 1150\t\r\n 1151\t # Build CSV headers\r\n 1152\t headers = [\r\n 1153\t \"Model\",\r\n 1154\t \"Case\",\r\n 1155\t \"Duration\",\r\n 1156\t \"Fixed_Diagram_Length\",\r\n 1157\t \"Failure_Reason\",\r\n 1158\t \"Tools_Used\",\r\n 1159\t ]\r\n 1160\t\r\n 1161\t for evaluator in sorted(all_evaluator_names):\r\n 1162\t headers.append(f\"Score_{evaluator}\")\r\n 1163\t\r\n 1164\t for metric in sorted(all_metric_names):\r\n 1165\t headers.append(f\"Metric_{metric}\")\r\n 1166\t\r\n 1167\t # Track how many model invocations were used (e.g., \"87/500\")\r\n 1168\t headers.append(REQUEST_USAGE_COLUMN)\r\n 1169\t\r\n 1170\t # Write the CSV file\r\n 1171\t with open(filepath, \"w\", newline=\"\", encoding=\"utf-8\") as csvfile:\r\n 1172\t writer = csv.writer(csvfile)\r\n 1173\t writer.writerow(headers)\r\n 1174\t\r\n 1175\t for case in report.cases:\r\n 1176\t row = [\r\n 1177\t model,\r\n 1178\t case.name,\r\n 1179\t case.task_duration,\r\n 1180\t len(case.output.fixed_diagram)\r\n 1181\t if case.output and case.output.fixed_diagram\r\n 1182\t else 0,\r\n 1183\t case.output.failure_reason if case.output else \"\",\r\n 1184\t \"|\".join(case.output.tools_used)\r\n 1185\t if case.output and case.output.tools_used\r\n 1186\t else \"\",\r\n 1187\t ]\r\n 1188\t\r\n 1189\t # Add evaluator scores\r\n 1190\t for evaluator in sorted(all_evaluator_names):\r\n 1191\t if evaluator in case.scores:\r\n 1192\t row.append(case.scores[evaluator].value)\r\n 1193\t else:\r\n 1194\t row.append(\"\")\r\n 1195\t\r\n 1196\t # Add metrics\r\n 1197\t for metric in sorted(all_metric_names):\r\n 1198\t if (\r\n 1199\t case.output\r\n 1200\t and hasattr(case.output, \"metrics\")\r\n 1201\t and case.output.metrics\r\n 1202\t and metric in case.output.metrics\r\n 1203\t ):\r\n 1204\t metric_value = case.output.metrics[metric]\r\n 1205\t if isinstance(metric_value, dict):\r\n 1206\t row.append(str(metric_value))\r\n 1207\t else:\r\n 1208\t row.append(metric_value)\r\n 1209\t else:\r\n 1210\t row.append(\"\")\r\n 1211\t\r\n 1212\t # Add request-usage summary as the last column\r\n 1213\t requests_used = None\r\n 1214\t if (\r\n 1215\t case.output\r\n 1216\t and hasattr(case.output, \"metrics\")\r\n 1217\t and isinstance(case.output.metrics, dict)\r\n 1218\t ):\r\n 1219\t requests_used = case.output.metrics.get(\"requests\")\r\n 1220\t\r\n",{"_670":874,"_662":663,"_672":81},{"_731":875,"_733":876,"_735":877,"_737":738,"_739":740,"_741":31,"_41":870,"_742":878,"_744":745,"_667":111,"_90":746}," 1140\t )\r\n 1141\t\r\n 1142\t # Collect all unique evaluator and metric names\r\n 1143\t all_evaluator_names = set()\r\n 1144\t all_metric_names = set()\r\n 1145\t\r\n 1146\t for case in report.cases:\r\n 1147\t all_evaluator_names.update(case.scores.keys())\r\n 1148\t if hasattr(case.output, \"metrics\") and case.output.metrics:\r\n 1149\t all_metric_names.update(case.output.metrics.keys())\r\n 1150\t\r\n 1151\t # Build CSV headers\r\n 1152\t headers = [\r\n 1153\t \"Model\",\r\n 1154\t \"Case\",\r\n 1155\t \"Duration\",\r\n 1156\t \"Fixed_Diagram_Length\",\r\n 1157\t \"Failure_Reason\",\r\n 1158\t \"Tools_Used\",\r\n 1159\t ]\r\n 1160\t\r\n 1161\t for evaluator in sorted(all_evaluator_names):\r\n 1162\t headers.append(f\"Score_{evaluator}\")\r\n 1163\t\r\n 1164\t for metric in sorted(all_metric_names):\r\n 1165\t headers.append(f\"Metric_{metric}\")\r\n 1166\t\r\n 1167\t # Track how many model invocations were used (e.g., \"87/500\")\r\n 1168\t headers.append(REQUEST_USAGE_COLUMN)\r\n 1169\t\r\n 1170\t # Write the CSV file\r\n 1171\t with open(filepath, \"w\", newline=\"\", encoding=\"utf-8\") as csvfile:\r\n 1172\t writer = csv.writer(csvfile)\r\n 1173\t writer.writerow(headers)\r\n 1174\t\r\n 1175\t for case in report.cases:\r\n 1176\t row = [\r\n 1177\t model,\r\n 1178\t case.name,\r\n 1179\t case.task_duration,\r\n 1180\t len(case.output.fixed_diagram)\r\n 1181\t if case.output and case.output.fixed_diagram\r\n 1182\t else 0,\r\n 1183\t case.output.failure_reason if case.output else \"\",\r\n 1184\t \"|\".join(case.output.tools_used)\r\n 1185\t if case.output and case.output.tools_used\r\n 1186\t else \"\",\r\n 1187\t ]\r\n 1188\t\r\n 1189\t # Add evaluator scores\r\n 1190\t for evaluator in sorted(all_evaluator_names):\r\n 1191\t if evaluator in case.scores:\r\n 1192\t row.append(case.scores[evaluator].value)\r\n 1193\t else:\r\n 1194\t row.append(\"\")\r\n 1195\t\r\n 1196\t # Add metrics\r\n 1197\t for metric in sorted(all_metric_names):\r\n 1198\t if (\r\n 1199\t case.output\r\n 1200\t and hasattr(case.output, \"metrics\")\r\n 1201\t and case.output.metrics\r\n 1202\t and metric in case.output.metrics\r\n 1203\t ):\r\n 1204\t metric_value = case.output.metrics[metric]\r\n 1205\t if isinstance(metric_value, dict):\r\n 1206\t row.append(str(metric_value))\r\n 1207\t else:\r\n 1208\t row.append(metric_value)\r\n 1209\t else:\r\n 1210\t row.append(\"\")\r\n 1211\t\r\n 1212\t # Add request-usage summary as the last column\r\n 1213\t requests_used = None\r\n 1214\t if (\r\n 1215\t case.output\r\n 1216\t and hasattr(case.output, \"metrics\")\r\n 1217\t and isinstance(case.output.metrics, dict)\r\n 1218\t ):\r\n 1219\t requests_used = case.output.metrics.get(\"requests\")\r\n 1220\t\r\n","/bin/bash -lc 'nl -ba agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py | sed -n '\"'\"'1140,1220p'\"'\"''",[879],"35633",{"_733":880,"_297":881,"_216":882,"_90":751},"nl -ba agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py | sed -n '1140,1220p'","evals_pydantic_mcp.py","agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py",{"_670":884,"_662":663,"_672":81},{"_731":-5,"_733":876,"_735":885,"_737":738,"_739":-5,"_741":-5,"_41":870,"_742":878,"_744":745,"_667":755,"_90":746},[886],{"_733":880,"_297":881,"_216":882,"_90":751},{"_670":888,"_662":663,"_672":81},{"_759":889,"_722":870,"_41":-5,"_297":761,"_762":-5,"_90":763},"{\"cmd\":\"nl -ba agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py | sed -n '1140,1220p'\"}",{"_670":891,"_662":663,"_672":81},{"_673":-5,"_691":892,"_41":893,"_694":894,"_90":696},"gAAAAABpaAjxXHZjSvWPh-HzT5-a3wXjTKK254_SPa-DWdmN7v2Qge1RQuKBLDaeVh_uLRjRJ-I5rXCO6V2_4y1rn58i4Lt5Vto1m3nun-V21zNdLlvrcA4xi5oqkW9SDqsCJjIwiisRSBH2otMIPqk7SjLq8OzE3dcLXSIf0_gWr84PlKxNngHrJQQXWl5aa00GQahrGEvIRSBqE4KaLHKaNj_vx-4bJ9Ig5rvoxfQfKQxLNEz2SyAQ7LNeRM2DZ7mMdcv-sfBLnxGzyu8xjSAkGsPCkARANVSGCYKcrLO5xb0CspFyeTYQiddTBtlp_vij-JenJGScZp72SZFY-9GaKMJcaZ24IAxfljmSv6QepoTpK6LNJQEXMBZcZYl2nuXxrOwfzl3Px6YgAJmVvJxOO7WUl-HUuuAQZO_-TQBTiCddJx7Nq1zlLRcXV3VUhmFUVZwhT3OFr-J6y3erK7VbQ6NEG0nYbC5-1bTAMxmeBn6Po4Cv_9_ZBkz6fI8a2L0sYWyyyGX8HQFXkHJ2Q20PUucbtRZtXSnQT3e1LwWqftYK2ttmwjk2fjScGfNUXlg8DYWRHB6foQplEmgoz9LwFmY8L53XEWE0uGvBXniaVzPBSQK53ixZS7THaTSKvXEwQmxtqn0Hd5vq_ESRM6mFL6SUyCXX7AlGmj-J0imulNjKzElgi8OK-zov8hzUbEgwuA-dkZQvvNaiiObejvsTp5o3rYRmUCS4bvtBKm4txop7ErRmRoSc79aFMJVFv7LRqCIspDCj0YQu6oHTCSxrAZdjAAZIRc3-Drbtc7WDJdAFM_lCNtCvTDK21qG4Hmyruu9EP7510giDlfaT6HogCTqwwJNCH2EC7q7p_39yipog1sI-RDRqi6c_Rp9mUB6qtHwLuyLpyvdUEUChgYLkPfIGFYEIVw==","legacy_responseItem_reasoning_54",[895],{"_45":896,"_90":700},"**Preparing to write headers**\n\nI'm noting the need to add newlines around header additions to format them properly.",{"_670":898,"_662":663,"_672":81},{"_673":899,"_41":900,"_694":901,"_90":696},[896],"legacy_reasoning_53",[],{"_670":903,"_662":663,"_672":81},{"_673":904,"_41":900,"_694":905,"_90":696},[],[],{"_670":907,"_662":663,"_672":81},{"_722":908,"_724":909,"_90":726},"call_6zJDbJIRmqEIUxDNzVh3MSj3",{"_408":910,"_728":-5},"Chunk ID: 67a5cd\nWall time: 0.0512 seconds\nProcess exited with code 0\nOriginal token count: 660\nOutput:\n 60\t\r\n 61\t# Default model configurations\r\n 62\tDEFAULT_MODEL = \"gemini-2.5-pro-preview-06-05\"\r\n 63\t\r\n 64\t# Retry configuration\r\n 65\tRETRYABLE_HTTP_STATUS_CODES = {429, 500, 502, 503, 504}\r\n 66\t\r\n 67\t# Request limit configuration for calls to the model\r\n 68\tREQUEST_LIMIT = 500\r\n 69\tREQUEST_USAGE_COLUMN = \"Requests_Used\"\r\n 70\t\r\n 71\t# Defaults tuned for evaluation runs (avoid hammering endpoints on 429/5xx)\r\n 72\tMAX_RETRY_ATTEMPTS = 20\r\n 73\tBASE_RETRY_DELAY = 2.0 # seconds\r\n 74\tMAX_RETRY_DELAY = 60.0 # seconds\r\n 75\t\r\n 76\t# Dynamically import validate_mermaid_diagram from the mermaid_validator.py file\r\n 77\tmermaid_validator_path = str(get_mcp_server_path(\"mermaid_validator.py\"))\r\n 78\tspec = importlib.util.spec_from_file_location(\"mermaid_validator\", mermaid_validator_path)\r\n 79\tmermaid_validator = importlib.util.module_from_spec(spec)\r\n 80\tsys.modules[\"mermaid_validator\"] = mermaid_validator\r\n 81\tspec.loader.exec_module(mermaid_validator)\r\n 82\tvalidate_mermaid_diagram = mermaid_validator.validate_mermaid_diagram\r\n 83\t\r\n 84\t# ============================================================================\r\n 85\t# Retry Utilities\r\n 86\t# ============================================================================\r\n 87\t\r\n 88\t\r\n 89\tclass MermaidValidatorUnavailableError(RuntimeError):\r\n 90\t \"\"\"Raised when Mermaid validation cannot be performed.\r\n 91\t\r\n 92\t This is treated as *fatal* since it invalidates the evaluation (fail-fast).\r\n 93\t \"\"\"\r\n 94\t\r\n 95\t\r\n 96\tdef _iter_base_exceptions(exception: BaseException) -> Iterable[BaseException]:\r\n 97\t \"\"\"Yield underlying exceptions, flattening `ExceptionGroup`s.\"\"\"\r\n 98\t if isinstance(exception, BaseExceptionGroup):\r\n 99\t for exc in exception.exceptions:\r\n 100\t yield from _iter_base_exceptions(exc)\r\n 101\t else:\r\n 102\t yield exception\r\n 103\t\r\n 104\t\r\n 105\tMERMAID_VALIDATOR_ERROR_HINTS = (\r\n 106\t \"mermaid_validator\",\r\n 107\t \"mermaid-validator\",\r\n 108\t \"validate_mermaid_diagram\",\r\n 109\t)\r\n 110\t\r\n 111\t\r\n 112\tdef is_mermaid_validator_unavailable_error(exception: BaseException) -> bool:\r\n 113\t \"\"\"Best-effort detection for Mermaid validator MCP/server failures.\"\"\"\r\n 114\t if isinstance(exception, MermaidValidatorUnavailableError):\r\n 115\t return True\r\n 116\t text = f\"{type(exception).__name__}: {exception}\".lower()\r\n 117\t return any(hint in text for hint in MERMAID_VALIDATOR_ERROR_HINTS)\r\n 118\t\r\n 119\t\r\n 120\tdef compute_exponential_backoff_delay(\r\n",{"_670":912,"_662":663,"_672":81},{"_731":913,"_733":914,"_735":915,"_737":738,"_739":740,"_741":31,"_41":908,"_742":916,"_744":745,"_667":111,"_90":746}," 60\t\r\n 61\t# Default model configurations\r\n 62\tDEFAULT_MODEL = \"gemini-2.5-pro-preview-06-05\"\r\n 63\t\r\n 64\t# Retry configuration\r\n 65\tRETRYABLE_HTTP_STATUS_CODES = {429, 500, 502, 503, 504}\r\n 66\t\r\n 67\t# Request limit configuration for calls to the model\r\n 68\tREQUEST_LIMIT = 500\r\n 69\tREQUEST_USAGE_COLUMN = \"Requests_Used\"\r\n 70\t\r\n 71\t# Defaults tuned for evaluation runs (avoid hammering endpoints on 429/5xx)\r\n 72\tMAX_RETRY_ATTEMPTS = 20\r\n 73\tBASE_RETRY_DELAY = 2.0 # seconds\r\n 74\tMAX_RETRY_DELAY = 60.0 # seconds\r\n 75\t\r\n 76\t# Dynamically import validate_mermaid_diagram from the mermaid_validator.py file\r\n 77\tmermaid_validator_path = str(get_mcp_server_path(\"mermaid_validator.py\"))\r\n 78\tspec = importlib.util.spec_from_file_location(\"mermaid_validator\", mermaid_validator_path)\r\n 79\tmermaid_validator = importlib.util.module_from_spec(spec)\r\n 80\tsys.modules[\"mermaid_validator\"] = mermaid_validator\r\n 81\tspec.loader.exec_module(mermaid_validator)\r\n 82\tvalidate_mermaid_diagram = mermaid_validator.validate_mermaid_diagram\r\n 83\t\r\n 84\t# ============================================================================\r\n 85\t# Retry Utilities\r\n 86\t# ============================================================================\r\n 87\t\r\n 88\t\r\n 89\tclass MermaidValidatorUnavailableError(RuntimeError):\r\n 90\t \"\"\"Raised when Mermaid validation cannot be performed.\r\n 91\t\r\n 92\t This is treated as *fatal* since it invalidates the evaluation (fail-fast).\r\n 93\t \"\"\"\r\n 94\t\r\n 95\t\r\n 96\tdef _iter_base_exceptions(exception: BaseException) -> Iterable[BaseException]:\r\n 97\t \"\"\"Yield underlying exceptions, flattening `ExceptionGroup`s.\"\"\"\r\n 98\t if isinstance(exception, BaseExceptionGroup):\r\n 99\t for exc in exception.exceptions:\r\n 100\t yield from _iter_base_exceptions(exc)\r\n 101\t else:\r\n 102\t yield exception\r\n 103\t\r\n 104\t\r\n 105\tMERMAID_VALIDATOR_ERROR_HINTS = (\r\n 106\t \"mermaid_validator\",\r\n 107\t \"mermaid-validator\",\r\n 108\t \"validate_mermaid_diagram\",\r\n 109\t)\r\n 110\t\r\n 111\t\r\n 112\tdef is_mermaid_validator_unavailable_error(exception: BaseException) -> bool:\r\n 113\t \"\"\"Best-effort detection for Mermaid validator MCP/server failures.\"\"\"\r\n 114\t if isinstance(exception, MermaidValidatorUnavailableError):\r\n 115\t return True\r\n 116\t text = f\"{type(exception).__name__}: {exception}\".lower()\r\n 117\t return any(hint in text for hint in MERMAID_VALIDATOR_ERROR_HINTS)\r\n 118\t\r\n 119\t\r\n 120\tdef compute_exponential_backoff_delay(\r\n","/bin/bash -lc 'nl -ba agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py | sed -n '\"'\"'60,120p'\"'\"''",[917],"99944",{"_733":918,"_297":881,"_216":882,"_90":751},"nl -ba agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py | sed -n '60,120p'",{"_670":920,"_662":663,"_672":81},{"_731":-5,"_733":914,"_735":921,"_737":738,"_739":-5,"_741":-5,"_41":908,"_742":916,"_744":745,"_667":755,"_90":746},[922],{"_733":918,"_297":881,"_216":882,"_90":751},{"_670":924,"_662":663,"_672":81},{"_759":925,"_722":908,"_41":-5,"_297":761,"_762":-5,"_90":763},"{\"cmd\":\"nl -ba agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py | sed -n '60,120p'\"}",{"_670":927,"_662":663,"_672":81},{"_673":-5,"_691":928,"_41":929,"_694":930,"_90":696},"gAAAAABpaAjtmHuh-UnBpQEQgpQ4SPlwQFEqBvk_IzkP0Pdm062FRp5nuvR-RD9aIKRaNWr4fJ1loZXEpTwSDv05WhZeBfOBGsedkO6QVOEhwWnvNz77R3vr3DYSOshCP0nWny4-W5v7udzRT4SWUThfifFLqiOIgk8HJOIh1T3MwI8HbAjNGxpJ8Of6QWrlEBjY-TDgxY41Nd3XWdDkjKs3O-JHl8IPqQ_sVWOT0OJQihnPfYDCIdANd9owtFwO-aiCti1yCpaH1OTc_DwVRULMnbJu9xJKkmU4nYT8dx2c11JrwLlyhbYmLShicxj0g1Ufc2Rsxo07IhqLqFoDTVSO1ghIYA5W-lnZ8ZenUpuAIURlKwp3W8AMVJYl0fNVFZX0sQ6aJpECy7DpM8s9dSFS8nM8wwsva9sR5uPJqNge-0b3PVfyLbIKO3j2pwBEHSzzGjPvzCAn7AmCRfShvo2E6NspWOMMVs2VxXIIu92g9JHYVNoTXtCKBwI4yGFgFhtL6oLIsmMk0c1rLJGM28lvzrO6YQFFyCkJPeKmrXQfY1bpxJMOf7Ta0o3ZOcp8hpiJOQCBgAXt1KvRGMyHvWYVdWgQQC1kO8lFjKnF8T8b63iFyR6C1WDINJ2rQeCdeuAuwnRdnEICoca3NXmQHX_WyXbHb--Ia57YsaTy7cGxRBP3h7ZFu8TutiMNp9NlOulRNdtkx7DE_o2XJVdmQfg0GO_1xbMeDO5XOSol49Z0-bi5nXKEuHhtNPCoGkeAEMq4tyJF0La3UgiBnG2YPIC2lLpSEbTAncOjTomXusvc6zIJrjjA1nVTg_TmP8tttBT_6ztfMZltGKwyhHqFA6yxlYLhifJsBRph4oBA2IotDu4AIzrBlmWRTm8TMAh73dsKZxI6jcic5rWBDHPgURAPZCjL5DV7CCjRwvRrP0jHc5Z2JSqAuAg=","legacy_responseItem_reasoning_52",[931],{"_45":932,"_90":700},"**Planning line range for constants**\n\nI'm preparing to extract lines around 60-90 and near headers to accurately cite the constants section.",{"_670":934,"_662":663,"_672":81},{"_673":935,"_41":936,"_694":937,"_90":696},[932],"legacy_reasoning_51",[],{"_670":939,"_662":663,"_672":81},{"_673":940,"_41":936,"_694":941,"_90":696},[],[],{"_670":943,"_662":663,"_672":81},{"_722":944,"_724":945,"_90":726},"call_9AKp3RlMcho6EBXRAv1VokDt",{"_408":946,"_728":-5},"Chunk ID: f7763f\nWall time: 0.0513 seconds\nProcess exited with code 0\nOriginal token count: 70\nOutput:\n69:REQUEST_USAGE_COLUMN = \"Requests_Used\"\r\n359: \"request_limit\",\r\n703: # NOTE: request_limit is the most common limiter we hit in tool-loop failure modes.\r\n704: usage_limits = UsageLimits(request_limit=REQUEST_LIMIT)\r\n1168: headers.append(REQUEST_USAGE_COLUMN)\r\n",{"_670":948,"_662":663,"_672":81},{"_731":949,"_733":950,"_735":951,"_737":738,"_739":740,"_741":31,"_41":944,"_742":952,"_744":745,"_667":111,"_90":746},"69:REQUEST_USAGE_COLUMN = \"Requests_Used\"\r\n359: \"request_limit\",\r\n703: # NOTE: request_limit is the most common limiter we hit in tool-loop failure modes.\r\n704: usage_limits = UsageLimits(request_limit=REQUEST_LIMIT)\r\n1168: headers.append(REQUEST_USAGE_COLUMN)\r\n","/bin/bash -lc 'rg -n \"REQUEST_USAGE_COLUMN|request_limit\" agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py | head -n 20'",[953],"36809",{"_733":954,"_216":881,"_955":956,"_90":957},"rg -n 'REQUEST_USAGE_COLUMN|request_limit' agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py","query","REQUEST_USAGE_COLUMN|request_limit","search",{"_670":959,"_662":663,"_672":81},{"_731":-5,"_733":950,"_735":960,"_737":738,"_739":-5,"_741":-5,"_41":944,"_742":952,"_744":745,"_667":755,"_90":746},[961],{"_733":954,"_216":881,"_955":956,"_90":957},{"_670":963,"_662":663,"_672":81},{"_759":964,"_722":944,"_41":-5,"_297":761,"_762":-5,"_90":763},"{\"cmd\":\"rg -n \\\"REQUEST_USAGE_COLUMN|request_limit\\\" agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py | head -n 20\"}",{"_670":966,"_662":663,"_672":81},{"_673":-5,"_691":967,"_41":968,"_694":969,"_90":696},"gAAAAABpaAjocsl_g2hW26nVW3Ob-zn6Ojd5R7K3CsrO6KWTfsCnE2iazBznwI-VVhiIV_vSlxA3GnzXZ8mfcxn1Ayaz2W_fB8fb9DkgmGGjRPG9I81fV9vZPusyIwqS5tx0FSxV7bInKM47pvCt2aWFsihexhdxxzhWNK7QSV5LGe-p30OYkSKV1ZGR4gDEt70WPoySOD1N0ILh7bhIbi7LZSnKAvk3sN6DEvzeFPHfIXRUOgzdxIAiXOJCCMZoLerUp0VgWqR9pWcY5rCsN03Mol3RsLQfKCpNdYnF4pdMkMRwx4glggNFjCQvvmx4PLfvQqZCL5EfJS9Ts93bZ1SPiB_XQYgcHmHPwbRUy8gyqjs8YJY1k6LfSzXItUUObYp0ps5mqUk9D8GSQRilOf9G14SGg4RonVTG_7gjZ7fIDpWneQb1fW885rM65KSX4b7KPL6UyIDzudpIGMmozOYBpQpJ4T0-orvdNg8bskhO7QEpf7LbnAu9RfoSLU8yDJY-xR_4WiKDkbkcd4Yji6NvCDGaK2CaTDj0mBhHrTxLhNwqBip44MXKEoVyx0y7775ta-OqzhVUaaY8KAZkM333Z1wI0SEBcszORDfjVdxB9igl8bja6UYlHDg4756ZP5eoClMjyTeLf7p2YgxW_j8bjZW2zHMgjUoJFyPsZLz5eHYD0B6Kg3jfAV4_VTYX2UERLTLNPQPiiuyZ8ccf5i65VboAt-WW_AvwBuY2z3UVB4yNDgikou7xkc-bim_gEODzJh1yXR9CHslLqlB4P12MDZ2bHG_xFQQ5wyOwh5uzuVGPW9gxvPzmnXRezI8IBgYbmhqbO6yGS3efB3B66rlscgHsHXFBQaGQ0meMXUAl_0CQ7kXoHl8DVdY5LMbnmvwUIbV-ClNs5HTd9Ti9NW0dZio7C4RcCS06_HAHiEbJLb_k7e1FNaf7MKumJYInf2MpxsNNrAIhC7vXW7GIktG5EiTM55e16Xt3Zpk4yyNVomIRHTLU9AU=","legacy_responseItem_reasoning_50",[970],{"_45":971,"_90":700},"**Locating request usage column constant**\n\nPlanning to search for the REQUEST_USAGE_COLUMN constant inside evals or evals_pydantic_mcp files to find the relevant column name for usage.",{"_670":973,"_662":663,"_672":81},{"_673":974,"_41":975,"_694":976,"_90":696},[971],"legacy_reasoning_49",[],{"_670":978,"_662":663,"_672":81},{"_673":979,"_41":975,"_694":980,"_90":696},[],[],{"_670":982,"_662":663,"_672":81},{"_722":983,"_724":984,"_90":726},"call_iNA2zAgdCMyEAaCKecLJCP36",{"_408":985,"_728":-5},"Chunk ID: 5fba0a\nWall time: 0.0518 seconds\nProcess exited with code 1\nOriginal token count: 0\nOutput:\n",{"_670":987,"_662":663,"_672":81},{"_731":315,"_733":988,"_735":989,"_737":738,"_739":740,"_741":990,"_41":983,"_742":991,"_744":745,"_667":992,"_90":746},"/bin/bash -lc 'rg -n \"Requests_Used\" agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py'",[993],1,"12156","failed",{"_733":994,"_216":995,"_955":996,"_90":957},"rg -n Requests_Used agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py","run_multi_evals.py","Requests_Used",{"_670":998,"_662":663,"_672":81},{"_731":-5,"_733":988,"_735":999,"_737":738,"_739":-5,"_741":-5,"_41":983,"_742":991,"_744":745,"_667":755,"_90":746},[1000],{"_733":994,"_216":995,"_955":996,"_90":957},{"_670":1002,"_662":663,"_672":81},{"_759":1003,"_722":983,"_41":-5,"_297":761,"_762":-5,"_90":763},"{\"cmd\":\"rg -n \\\"Requests_Used\\\" agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py\"}",{"_670":1005,"_662":663,"_672":81},{"_673":-5,"_691":1006,"_41":1007,"_694":1008,"_90":696},"gAAAAABpaAjlYbEXeHr4u2enwwrnSUgG-g-gtTRRcw_QIV2jw1Hc2r27bF19UDpPXVfg7hrlY2KQ4HFXlZKXanzFlGKT-pwOzlGpYlzfO-8cKGoGyi_bmGM9zUxE1cYvgJz_k-XA5_9beXtSkMtMzGcuSO3_cmaZzXM9xS63FBfBYGP4MjppqZIh2eoYxaYcYh1e5Uj5DLpgsJ72BDyuqJm2TjkRlAxjNt2dL8yqUJZ-DWM_OP_zsER3jxWdBIN4K0kgnNQa2yBgBrpTaiGXBbQeT6UQKXV8fvtEG77jBYElx6F2tHy7fMK-naPIfn8IlTAhfRNQtQ0HH-TMtbaKOPStglppw9gYgf88iw20aTlOkPG6rkVN69TUuCed9xAmbjpPVNjFmKsdLb6AH3L_lyK9g_AHbu196n212BN1i1dgI3sSaif6zA03zU_3RV--c08UlWCTwG0-LpCo6FFe38nm7maTwQNRjiEZrjSLoiJsfgtnk4rLChCtZWYjIw7acjXAGsSFKxgMWNEEYZqCgVRVBIHBJk9NHF3EuuAT1G4YQ3SxZP6P8sVld7wHCuJWOd1vOcbSR_27_kFLxSUjWaMmLvKaQezSwK0rSMlqwvlordBeL0OHBoy2z7of-SifN1sRsnJ-IJ6A8gHi1Dota0apR1DrjbQuMvHkvIkKuiMUYsDObNBP4Yw_QY89ZGqEoCWOr0uF1OoyJIgU9kXB0TLUw-Vw3kywtec9C487JAbYp-k-hdX47RLB8FZksviih1NHty-EZiro13LxIrMHwVEvz3FQ1v1OBLJwXohj7C4SVGBogLqIYcvdgEubSYnWXkhk_EkFXDketyN3AEB5OdOFexsrESoP52Hs-NhGYDTocoUgYfp3wGmXbwrTN2mc8V0OGvCvDWzZ8FySS_1mXw1L-ctbdhCRtMJg88ah3ugTdzHduKrZ1Fc5pr5xUcPxqD4Q7ajYghnpzDf6bVu10DsN-v4c7esNEfsTO38ZIewHNfqKH9cG5GpPhbjJlpxrjrBwqjwKtwT5QCpnDI20EcJSWzcGDfwwJRFEf84apyG-aGCNVyDu8sUX37Ok41xODLfVK4Owj7Vfc-5UoxGVm78-AdojrO_ei487drRLOpCwdW6mGo8jU6FAXsHgRxI-pBTdgWmJ4GPWpCeVky5f4kOJMvi1bB6CtxQFfs7UvTEOfa8qO3j2jJezIJoAyWT4dD8yNUdZGsRc6tpwyFpVGIeGBuHLAgRnEy2590ZscxB4hAkXmrVjyIeKnA2VN9LeigKXcU_xjkj2f9YMr45E0RyDKbtElzNGWA2SCBG3LMDR-aPyYsw7n4Ncpc92LVDIjbWIUUmtOim2o2U8Wl787GVMH_zfmB7kQI6LNOm0jF_w6T_2zuSDYG7NRHa3l4fxL39CksJbdCtjP-uN1MhF6g9ofiY0VS3ZZsaXnluYGbYOC31kqJllP53jZ-swJLy2df2YYSZXUX5KY3lStQp_hbnkVukqAFqWrrsdxcyyjXx-YKB-cRVaCR3ySdUwpSUc2QPer1QW49U5WQKtqdT_0Zk9_lyYmH3kIrv2nNEvrfdcVIjJGkgKixiffVJVQPUh7OxnjDjXsB0iZBszx3NuAtIUAPM1TUOuaw9ZdkvW2wHXJatxvm1Q71lfUsRaEWyZCD1rsGoq323y7_3EFWi-tGYHBEb-9R88Sofly8vAuuACzdwaCpkTgluKn6LNX0VjGHE-ja5TvDk9jTvAWmP4zG77zigeWX5cEfcsuN47kYdbpHxXAvrxmFuAxtN5rPbN2wK3VB-_l-Ia3chBZF0H7EhEwPIvhi99fdjGMD8iZVZWA9eWmiH3h3do9CTj_g8DpwYV-7jFZVR55VmNgry4zrI7er0ZRgPWNZOSz6gr49WnFhmstvelzU97fh3t3f3T0rGofpRWf5aZEwFu-Obsf3j7F5DyU-9_F3kZRXmkmjihaAxsnxgPcaTVYRH-QGfN5Pmwi2Vqg1LDnXLvOB1MfPWeXF8XteabdZ6tmy_qMOOzgppuoIs7KdsEX8IvfNwd2O9bzIVGqA5FfdEsJ2Y2D96xc7iL190C3BaX-VLEZaqvKEP8z75Adx76fehJcvw746YndRvgd2YECqo0o9DWX4ScXszuNlWapdQU6psgiSt1f_-WHiI8IRapAyIiKagE5zmQk-IbBEN4ppOaBrNQmI3UCGTXl2iu_upI2PCCZdvXG_rBGTKmIamtHotf-otOvG4l6gVoMZPK33sfHPwrWsIW_VQXbr4zxvG607z2fAAbHxhc0qpNjJSpplnJa91o73yd6XxG4Em0","legacy_responseItem_reasoning_48",[1009],{"_45":1010,"_90":700},"**Reviewing evaluation output columns**\n\nI'm verifying where the new \"Requests_Used\" column is added in run_multi_evals to cite it properly in the PR description, noting it impacts evaluation outputs and preprocess merging with new debug traces and costs.json updates.",{"_670":1012,"_662":663,"_672":81},{"_673":1013,"_41":1014,"_694":1015,"_90":696},[1010],"legacy_reasoning_47",[],{"_670":1017,"_662":663,"_672":81},{"_673":1018,"_41":1014,"_694":1019,"_90":696},[],[],{"_670":1021,"_662":663,"_672":81},{"_722":1022,"_724":1023,"_90":726},"call_8Rpr5wx5hx1oyybbSS1OvJS0",{"_408":1024,"_728":-5},"Chunk ID: b00415\nWall time: 0.0515 seconds\nProcess exited with code 0\nOriginal token count: 426\nOutput:\n 50\t ) as temp_file:\r\n 51\t temp_file.write(diagram_text)\r\n 52\t temp_file_path = temp_file.name\r\n 53\t\r\n 54\t output_file = tempfile.NamedTemporaryFile(suffix=\".png\", delete=False)\r\n 55\t output_file.close()\r\n 56\t output_file_name = output_file.name\r\n 57\t\r\n 58\t puppeteer_config = {\"args\": [\"--no-sandbox\", \"--disable-setuid-sandbox\"]}\r\n 59\t with tempfile.NamedTemporaryFile(\r\n 60\t suffix=\".json\", mode=\"w\", delete=False\r\n 61\t ) as config_file:\r\n 62\t json.dump(puppeteer_config, config_file)\r\n 63\t puppeteer_config_path = config_file.name\r\n 64\t\r\n 65\t result = subprocess.run(\r\n 66\t [\r\n 67\t \"npx\",\r\n 68\t \"-y\",\r\n 69\t \"@mermaid-js/mermaid-cli@11.4.2\",\r\n 70\t \"-i\",\r\n 71\t temp_file_path,\r\n 72\t \"-o\",\r\n 73\t output_file_name,\r\n 74\t \"--puppeteerConfigFile\",\r\n 75\t puppeteer_config_path,\r\n 76\t ],\r\n 77\t capture_output=True,\r\n 78\t text=True,\r\n 79\t )\r\n 80\t\r\n 81\t if result.returncode == 0:\r\n 82\t with open(output_file_name, \"rb\") as f:\r\n 83\t diagram_image = base64.b64encode(f.read()).decode(\"utf-8\")\r\n 84\t\r\n 85\t return MermaidValidationResult(\r\n 86\t is_valid=True, error_message=None, diagram_image=diagram_image\r\n 87\t )\r\n 88\t else:\r\n 89\t return MermaidValidationResult(\r\n 90\t is_valid=False,\r\n",{"_670":1026,"_662":663,"_672":81},{"_731":1027,"_733":1028,"_735":1029,"_737":738,"_739":740,"_741":31,"_41":1022,"_742":1030,"_744":745,"_667":111,"_90":746}," 50\t ) as temp_file:\r\n 51\t temp_file.write(diagram_text)\r\n 52\t temp_file_path = temp_file.name\r\n 53\t\r\n 54\t output_file = tempfile.NamedTemporaryFile(suffix=\".png\", delete=False)\r\n 55\t output_file.close()\r\n 56\t output_file_name = output_file.name\r\n 57\t\r\n 58\t puppeteer_config = {\"args\": [\"--no-sandbox\", \"--disable-setuid-sandbox\"]}\r\n 59\t with tempfile.NamedTemporaryFile(\r\n 60\t suffix=\".json\", mode=\"w\", delete=False\r\n 61\t ) as config_file:\r\n 62\t json.dump(puppeteer_config, config_file)\r\n 63\t puppeteer_config_path = config_file.name\r\n 64\t\r\n 65\t result = subprocess.run(\r\n 66\t [\r\n 67\t \"npx\",\r\n 68\t \"-y\",\r\n 69\t \"@mermaid-js/mermaid-cli@11.4.2\",\r\n 70\t \"-i\",\r\n 71\t temp_file_path,\r\n 72\t \"-o\",\r\n 73\t output_file_name,\r\n 74\t \"--puppeteerConfigFile\",\r\n 75\t puppeteer_config_path,\r\n 76\t ],\r\n 77\t capture_output=True,\r\n 78\t text=True,\r\n 79\t )\r\n 80\t\r\n 81\t if result.returncode == 0:\r\n 82\t with open(output_file_name, \"rb\") as f:\r\n 83\t diagram_image = base64.b64encode(f.read()).decode(\"utf-8\")\r\n 84\t\r\n 85\t return MermaidValidationResult(\r\n 86\t is_valid=True, error_message=None, diagram_image=diagram_image\r\n 87\t )\r\n 88\t else:\r\n 89\t return MermaidValidationResult(\r\n 90\t is_valid=False,\r\n","/bin/bash -lc 'nl -ba mcp_servers/mermaid_validator.py | sed -n '\"'\"'50,90p'\"'\"''",[1031],"1405",{"_733":1032,"_297":1033,"_216":1034,"_90":751},"nl -ba mcp_servers/mermaid_validator.py | sed -n '50,90p'","mermaid_validator.py","mcp_servers/mermaid_validator.py",{"_670":1036,"_662":663,"_672":81},{"_731":-5,"_733":1028,"_735":1037,"_737":738,"_739":-5,"_741":-5,"_41":1022,"_742":1030,"_744":745,"_667":755,"_90":746},[1038],{"_733":1032,"_297":1033,"_216":1034,"_90":751},{"_670":1040,"_662":663,"_672":81},{"_759":1041,"_722":1022,"_41":-5,"_297":761,"_762":-5,"_90":763},"{\"cmd\":\"nl -ba mcp_servers/mermaid_validator.py | sed -n '50,90p'\"}",{"_670":1043,"_662":663,"_672":81},{"_673":-5,"_691":1044,"_41":1045,"_694":1046,"_90":696},"gAAAAABpaAjcoQRuH3LP-SCKR9R61PYhMTQ8_Mf4AnMekX2_OC5mmhcUzfSSFWwPupSmcSA0TGDs4qzyRJFeEZ0AdUR5-4qM3UUk2OR5Anow-lWD-ozTSqQWCXOtYnbfmP7-29yXBTr82_j6q0B7aoUzE24jr1C3WYsOeMM5DBE0pVXd6aLwDO7_UllMPNZnIsmAUTd2XnKPhb83Z-uXebG2wZTvhYt2EcmmaIRM445Sfk1QRWlq-Wyk2DHXd6fJS5vfF4j5G29gknM28HYIlTdDn95r8DupVp796V77MXifMoMH6eToSqiMs0B23w6ehJN67DEBtFji9EL_gtRa9Xq4UdB-l06NXnXntfjQb7zF3pWerSP7OW5BY9khdw0Xx8a-HNg8bf2bvavtEBz_VhXncATmIP1_R-AluSoGA-NRZf0ZO0o8YrMGPD795mNQVyPoSGH0lpskb7WJUGs76lLslFCEuTgHd_9ZaLSq0O8n4fQMX8LF5CGFQKTQwH8B51h3dKi5b7RMcCn6oOr9nePEx4i_zyAwSRHqAOsUnIOxouZlBCio1eMCfuY9CqgSznn4u33rR_VwZ0IDzc0MMvAf4TZDJiQmYOlDL-MQHO236J9qsF5b59rixQxjC4AmzyxHwTmWIUGZRgRQqrbmqRUh6Jg7bSbzSQrcMUmVNZWoNYMj-25lNLL_kzvenqnPGj2uYIGLUPD__80RkT4v2bpXZbJ4ctAgx-046MAJw-SGgD8wNe7hzQy1qrEWN40wEjcA11PB-LvyzXRxiOASCGDuV34V22pFGQXNC_uRc9YB2wt9J7xx6FiC_g6ja1vyyJ-QseSOYEjyXSSR8-_a4YuFSJNAbkGx4FbSE-xwfNlDYlsIMQNduvIcTul-oGsIhJHbTnlnegOOURcDPIF-5HjZn0MCv9aIgxt4zIBC_TY9buAK9quf6k5Atmc3kyjMf9oAndffT0UwG7wXF1wpsBbhn6yV-ODEiHLtvZpfBDF2AaYZhyT0m363C68Z3_YtqULjuVKQbjg_h5wampiv2gLBKcGDfp3WPKC99fhGIRqHnDiSz-H_cWGXbugTtEf5eza5qZhjxeG9EKU_6IhYmcN7Im9o2EFob72jRSu6IQzLTtJ9GdKCf6AIcEV4010Iz5xCC6Jru5LuRmrRyQNzyW1Ry-5gfzlcBEK-DbT148wzm4lsIuSlTC9VfUltswdPIrMQbnJaA9Gy","legacy_responseItem_reasoning_46",[1047],{"_45":1048,"_90":700},"**Noting mermaid validator pinning**\n\nPlanning to mention pinning the mermaid CLI version for the validator with a citation from the mermaid_validator.py file, without adding lines for provider detection or .env examples now.",{"_670":1050,"_662":663,"_672":81},{"_673":1051,"_41":1052,"_694":1053,"_90":696},[1048],"legacy_reasoning_45",[],{"_670":1055,"_662":663,"_672":81},{"_673":1056,"_41":1052,"_694":1057,"_90":696},[],[],{"_670":1059,"_662":663,"_672":81},{"_722":1060,"_724":1061,"_90":726},"call_xdrfS8wu0M4bnk1pJcUKKcM3",{"_408":1062,"_728":-5},"Chunk ID: 0f9f2d\nWall time: 0.0506 seconds\nProcess exited with code 0\nOriginal token count: 2114\nOutput:\n 1\t#!/usr/bin/env python3\r\n 2\timport argparse\r\n 3\timport json\r\n 4\timport re\r\n 5\timport sys\r\n 6\tfrom datetime import datetime\r\n 7\tfrom pathlib import Path\r\n 8\tfrom typing import Any, Dict, List\r\n 9\t\r\n 10\timport pandas as pd\r\n 11\t\r\n 12\t# Add parent directory to path to import modules\r\n 13\tsys.path.append(str(Path(__file__).parent.parent))\r\n 14\t\r\n 15\tfrom agents_mcp_usage.evaluations.mermaid_evals.dashboard_config import DEFAULT_CONFIG\r\n 16\tfrom agents_mcp_usage.evaluations.mermaid_evals.schemas import DashboardConfig\r\n 17\tfrom agents_mcp_usage.evaluations.mermaid_evals.evals_pydantic_mcp import (\r\n 18\t REQUEST_USAGE_COLUMN,\r\n 19\t)\r\n 20\tfrom agents_mcp_usage.utils import get_project_root\r\n 21\t\r\n 22\t\r\n 23\tREASONING_SUFFIX_RE = re.compile(r\"\\s*\\((low|medium|high)\\)\\s*$\", re.IGNORECASE)\r\n 24\t\r\n 25\t\r\n 26\tdef normalize_model_name(model_name: Any) -> Any:\r\n 27\t \"\"\"Normalize model names for cost lookup.\r\n 28\t\r\n 29\t - Strip trailing reasoning hints like \"(medium)\".\r\n 30\t - Map OpenAI Responses provider prefix to the standard OpenAI key used in costs.json.\r\n 31\t - Add an \"openai:\" prefix for bare GPT model names.\r\n 32\t \"\"\"\r\n 33\t\r\n 34\t if not isinstance(model_name, str):\r\n 35\t return model_name\r\n 36\t\r\n 37\t name = model_name.strip()\r\n 38\t name = REASONING_SUFFIX_RE.sub(\"\", name).strip()\r\n 39\t\r\n 40\t if name.startswith(\"openai-responses:\"):\r\n 41\t name = \"openai:\" + name.split(\":\", 1)[1]\r\n 42\t elif name.startswith(\"gpt-\") and \":\" not in name:\r\n 43\t name = f\"openai:{name}\"\r\n 44\t\r\n 45\t return name\r\n 46\t\r\n 47\tdef load_model_costs(file_path: Path) -> Dict[str, Any]:\r\n 48\t \"\"\"Load model costs from JSON file.\"\"\"\r\n 49\t with open(file_path, 'r') as f:\r\n 50\t data = json.load(f)\r\n 51\t \r\n 52\t # Convert \"inf\" strings to actual infinity\r\n 53\t def convert_inf_strings(obj: Any) -> Any:\r\n 54\t if isinstance(obj, dict):\r\n 55\t return {k: convert_inf_strings(v) for k, v in obj.items()}\r\n 56\t elif isinstance(obj, list):\r\n 57\t return [convert_inf_strings(v) for v in obj]\r\n 58\t elif obj == \"inf\":\r\n 59\t return float(\"inf\")\r\n 60\t return obj\r\n 61\t \r\n 62\t # Extract model_costs from the loaded data\r\n 63\t model_costs = data.get('model_costs', {})\r\n 64\t return convert_inf_strings(model_costs)\r\n 65\t\r\n 66\tdef get_price_for_tokens(token_count: int, price_tiers: List[Dict]) -> float:\r\n 67\t \"\"\"Find the correct price for a given number of tokens from a list of tiers.\"\"\"\r\n 68\t for tier in price_tiers:\r\n 69\t if token_count <= tier[\"up_to\"]:\r\n 70\t return tier[\"price\"]\r\n 71\t return price_tiers[-1][\"price\"] # Fallback to the highest tier price\r\n 72\t\r\n 73\tdef calculate_costs(df: pd.DataFrame, cost_config: Dict, config: DashboardConfig) -> pd.DataFrame:\r\n 74\t \"\"\"Calculate input, output, and total costs for each run based on tiered pricing.\r\n 75\t \r\n 76\t Sets cost to 0 if Score_UsageLimitNotExceeded == 0.\r\n 77\t \"\"\"\r\n 78\t df_with_costs = df.copy()\r\n 79\t \r\n 80\t # Get cost calculation config from dashboard config\r\n 81\t cost_calc_config = config.cost_calculation\r\n 82\t input_token_cols = cost_calc_config.input_token_cols\r\n 83\t output_token_cols = cost_calc_config.output_token_cols\r\n 84\t \r\n 85\t # Initialize cost columns\r\n 86\t df_with_costs[\"input_cost\"] = 0.0\r\n 87\t df_with_costs[\"output_cost\"] = 0.0\r\n 88\t df_with_costs[\"total_cost\"] = 0.0\r\n 89\t \r\n 90\t for idx, row in df_with_costs.iterrows():\r\n 91\t # Check if usage limit was exceeded - if so, cost is 0\r\n 92\t if row.get(\"Score_UsageLimitNotExceeded\", 1) == 0:\r\n 93\t continue\r\n 94\t \r\n 95\t model = row.get(\"Model\")\r\n 96\t model_key = normalize_model_name(model)\r\n 97\t model_costs = cost_config.get(model_key)\r\n 98\t\r\n 99\t if not model_costs:\r\n 100\t continue\r\n 101\t \r\n 102\t try:\r\n 103\t # Calculate token counts\r\n 104\t input_tokens = sum(row.get(col, 0) or 0 for col in input_token_cols)\r\n 105\t output_tokens = sum(row.get(col, 0) or 0 for col in output_token_cols)\r\n 106\t thinking_tokens = row.get(\"thinking_tokens\", 0) or 0\r\n 107\t non_thinking_output_tokens = output_tokens - thinking_tokens\r\n 108\t \r\n 109\t total_tokens = input_tokens + output_tokens\r\n 110\t \r\n 111\t # Calculate input cost\r\n 112\t input_price_tiers = model_costs.get(\"input\", [])\r\n 113\t if input_price_tiers:\r\n 114\t input_price = get_price_for_tokens(total_tokens, input_price_tiers)\r\n 115\t input_cost = (input_tokens / 1_000_000) * input_price\r\n 116\t else:\r\n 117\t input_cost = 0.0\r\n 118\t \r\n 119\t # Calculate output cost\r\n 120\t output_cost = 0.0\r\n 121\t output_pricing = model_costs.get(\"output\", {})\r\n 122\t \r\n 123\t if \"thinking\" in output_pricing and thinking_tokens > 0:\r\n 124\t thinking_price_tiers = output_pricing[\"thinking\"]\r\n 125\t thinking_price = get_price_for_tokens(total_tokens, thinking_price_tiers)\r\n 126\t output_cost += (thinking_tokens / 1_000_000) * thinking_price\r\n 127\t \r\n 128\t if \"non_thinking\" in output_pricing and non_thinking_output_tokens > 0:\r\n 129\t non_thinking_price_tiers = output_pricing[\"non_thinking\"]\r\n 130\t non_thinking_price = get_price_for_tokens(total_tokens, non_thinking_price_tiers)\r\n 131\t output_cost += (non_thinking_output_tokens / 1_000_000) * non_thinking_price\r\n 132\t \r\n 133\t elif \"default\" in output_pricing:\r\n 134\t default_price_tiers = output_pricing[\"default\"]\r\n 135\t default_price = get_price_for_tokens(total_tokens, default_price_tiers)\r\n 136\t output_cost += (output_tokens / 1_000_000) * default_price\r\n 137\t \r\n 138\t df_with_costs.at[idx, \"input_cost\"] = input_cost\r\n 139\t df_with_costs.at[idx, \"output_cost\"] = output_cost\r\n 140\t df_with_costs.at[idx, \"total_cost\"] = input_cost + output_cost\r\n 141\t \r\n 142\t except (TypeError, KeyError, IndexError) as e:\r\n 143\t print(f\"Cost calculation error for model {model} at row {idx}: {e}\")\r\n 144\t \r\n 145\t return df_with_costs\r\n 146\t\r\n 147\tdef parse_metric_details(metric_details_str):\r\n 148\t \"\"\"Safely parse JSON string from Metric_details column.\"\"\"\r\n 149\t if pd.isna(metric_details_str) or not metric_details_str:\r\n 150\t return {}\r\n 151\t try:\r\n 152\t return json.loads(metric_details_str.replace(\"'\", '\"'))\r\n 153\t except (json.JSONDecodeError, TypeError):\r\n 154\t return {}\r\n 155\t\r\n 156\tdef calculate_failure_analysis_data(df):\r\n 157\t \"\"\"Calculate failure counts by model and failure type.\"\"\"\r\n 158\t failure_series = [\r\n 159\t {\"name\": \"Invalid Diagram\", \"column\": \"Score_MermaidDiagramValid\", \"condition\": \"== 0\"},\r\n 160\t {\"name\": \"MCP Tool Failure\", \"column\": \"Score_UsedBothMCPTools\", \"condition\": \"< 1\"},\r\n 161\t {\"name\": \"Usage Limit Exceeded\", \"column\": \"Score_UsageLimitNotExceeded\", \"condition\": \"== 0\"},\r\n 162\t ]\r\n 163\t \r\n 164\t models = sorted(df[\"Model\"].unique())\r\n 165\t failure_data = []\r\n 166\t \r\n 167\t for model in models:\r\n 168\t model_data = df[df[\"Model\"] == model]\r\n 169\t failure_counts = {\"Model\": model}\r\n 170\t \r\n 171\t for series in failure_series:\r\n 172\t condition_str = f\"`{series['column']}` {series['condition']}\"\r\n 173\t count = model_data.eval(condition_str).sum()\r\n 174\t failure_counts[series[\"name\"]] = int(count)\r\n 175\t \r\n 176\t failure_data.append(failure_counts)\r\n 177\t \r\n 178\t return failure_data\r\n 179\t\r\n 180\tdef process_csv_for_static_site(csv_path):\r\n",{"_670":1064,"_662":663,"_672":81},{"_731":1065,"_733":1066,"_735":1067,"_737":738,"_739":801,"_741":31,"_41":1060,"_742":1068,"_744":745,"_667":111,"_90":746}," 1\t#!/usr/bin/env python3\r\n 2\timport argparse\r\n 3\timport json\r\n 4\timport re\r\n 5\timport sys\r\n 6\tfrom datetime import datetime\r\n 7\tfrom pathlib import Path\r\n 8\tfrom typing import Any, Dict, List\r\n 9\t\r\n 10\timport pandas as pd\r\n 11\t\r\n 12\t# Add parent directory to path to import modules\r\n 13\tsys.path.append(str(Path(__file__).parent.parent))\r\n 14\t\r\n 15\tfrom agents_mcp_usage.evaluations.mermaid_evals.dashboard_config import DEFAULT_CONFIG\r\n 16\tfrom agents_mcp_usage.evaluations.mermaid_evals.schemas import DashboardConfig\r\n 17\tfrom agents_mcp_usage.evaluations.mermaid_evals.evals_pydantic_mcp import (\r\n 18\t REQUEST_USAGE_COLUMN,\r\n 19\t)\r\n 20\tfrom agents_mcp_usage.utils import get_project_root\r\n 21\t\r\n 22\t\r\n 23\tREASONING_SUFFIX_RE = re.compile(r\"\\s*\\((low|medium|high)\\)\\s*$\", re.IGNORECASE)\r\n 24\t\r\n 25\t\r\n 26\tdef normalize_model_name(model_name: Any) -> Any:\r\n 27\t \"\"\"Normalize model names for cost lookup.\r\n 28\t\r\n 29\t - Strip trailing reasoning hints like \"(medium)\".\r\n 30\t - Map OpenAI Responses provider prefix to the standard OpenAI key used in costs.json.\r\n 31\t - Add an \"openai:\" prefix for bare GPT model names.\r\n 32\t \"\"\"\r\n 33\t\r\n 34\t if not isinstance(model_name, str):\r\n 35\t return model_name\r\n 36\t\r\n 37\t name = model_name.strip()\r\n 38\t name = REASONING_SUFFIX_RE.sub(\"\", name).strip()\r\n 39\t\r\n 40\t if name.startswith(\"openai-responses:\"):\r\n 41\t name = \"openai:\" + name.split(\":\", 1)[1]\r\n 42\t elif name.startswith(\"gpt-\") and \":\" not in name:\r\n 43\t name = f\"openai:{name}\"\r\n 44\t\r\n 45\t return name\r\n 46\t\r\n 47\tdef load_model_costs(file_path: Path) -> Dict[str, Any]:\r\n 48\t \"\"\"Load model costs from JSON file.\"\"\"\r\n 49\t with open(file_path, 'r') as f:\r\n 50\t data = json.load(f)\r\n 51\t \r\n 52\t # Convert \"inf\" strings to actual infinity\r\n 53\t def convert_inf_strings(obj: Any) -> Any:\r\n 54\t if isinstance(obj, dict):\r\n 55\t return {k: convert_inf_strings(v) for k, v in obj.items()}\r\n 56\t elif isinstance(obj, list):\r\n 57\t return [convert_inf_strings(v) for v in obj]\r\n 58\t elif obj == \"inf\":\r\n 59\t return float(\"inf\")\r\n 60\t return obj\r\n 61\t \r\n 62\t # Extract model_costs from the loaded data\r\n 63\t model_costs = data.get('model_costs', {})\r\n 64\t return convert_inf_strings(model_costs)\r\n 65\t\r\n 66\tdef get_price_for_tokens(token_count: int, price_tiers: List[Dict]) -> float:\r\n 67\t \"\"\"Find the correct price for a given number of tokens from a list of tiers.\"\"\"\r\n 68\t for tier in price_tiers:\r\n 69\t if token_count <= tier[\"up_to\"]:\r\n 70\t return tier[\"price\"]\r\n 71\t return price_tiers[-1][\"price\"] # Fallback to the highest tier price\r\n 72\t\r\n 73\tdef calculate_costs(df: pd.DataFrame, cost_config: Dict, config: DashboardConfig) -> pd.DataFrame:\r\n 74\t \"\"\"Calculate input, output, and total costs for each run based on tiered pricing.\r\n 75\t \r\n 76\t Sets cost to 0 if Score_UsageLimitNotExceeded == 0.\r\n 77\t \"\"\"\r\n 78\t df_with_costs = df.copy()\r\n 79\t \r\n 80\t # Get cost calculation config from dashboard config\r\n 81\t cost_calc_config = config.cost_calculation\r\n 82\t input_token_cols = cost_calc_config.input_token_cols\r\n 83\t output_token_cols = cost_calc_config.output_token_cols\r\n 84\t \r\n 85\t # Initialize cost columns\r\n 86\t df_with_costs[\"input_cost\"] = 0.0\r\n 87\t df_with_costs[\"output_cost\"] = 0.0\r\n 88\t df_with_costs[\"total_cost\"] = 0.0\r\n 89\t \r\n 90\t for idx, row in df_with_costs.iterrows():\r\n 91\t # Check if usage limit was exceeded - if so, cost is 0\r\n 92\t if row.get(\"Score_UsageLimitNotExceeded\", 1) == 0:\r\n 93\t continue\r\n 94\t \r\n 95\t model = row.get(\"Model\")\r\n 96\t model_key = normalize_model_name(model)\r\n 97\t model_costs = cost_config.get(model_key)\r\n 98\t\r\n 99\t if not model_costs:\r\n 100\t continue\r\n 101\t \r\n 102\t try:\r\n 103\t # Calculate token counts\r\n 104\t input_tokens = sum(row.get(col, 0) or 0 for col in input_token_cols)\r\n 105\t output_tokens = sum(row.get(col, 0) or 0 for col in output_token_cols)\r\n 106\t thinking_tokens = row.get(\"thinking_tokens\", 0) or 0\r\n 107\t non_thinking_output_tokens = output_tokens - thinking_tokens\r\n 108\t \r\n 109\t total_tokens = input_tokens + output_tokens\r\n 110\t \r\n 111\t # Calculate input cost\r\n 112\t input_price_tiers = model_costs.get(\"input\", [])\r\n 113\t if input_price_tiers:\r\n 114\t input_price = get_price_for_tokens(total_tokens, input_price_tiers)\r\n 115\t input_cost = (input_tokens / 1_000_000) * input_price\r\n 116\t else:\r\n 117\t input_cost = 0.0\r\n 118\t \r\n 119\t # Calculate output cost\r\n 120\t output_cost = 0.0\r\n 121\t output_pricing = model_costs.get(\"output\", {})\r\n 122\t \r\n 123\t if \"thinking\" in output_pricing and thinking_tokens > 0:\r\n 124\t thinking_price_tiers = output_pricing[\"thinking\"]\r\n 125\t thinking_price = get_price_for_tokens(total_tokens, thinking_price_tiers)\r\n 126\t output_cost += (thinking_tokens / 1_000_000) * thinking_price\r\n 127\t \r\n 128\t if \"non_thinking\" in output_pricing and non_thinking_output_tokens > 0:\r\n 129\t non_thinking_price_tiers = output_pricing[\"non_thinking\"]\r\n 130\t non_thinking_price = get_price_for_tokens(total_tokens, non_thinking_price_tiers)\r\n 131\t output_cost += (non_thinking_output_tokens / 1_000_000) * non_thinking_price\r\n 132\t \r\n 133\t elif \"default\" in output_pricing:\r\n 134\t default_price_tiers = output_pricing[\"default\"]\r\n 135\t default_price = get_price_for_tokens(total_tokens, default_price_tiers)\r\n 136\t output_cost += (output_tokens / 1_000_000) * default_price\r\n 137\t \r\n 138\t df_with_costs.at[idx, \"input_cost\"] = input_cost\r\n 139\t df_with_costs.at[idx, \"output_cost\"] = output_cost\r\n 140\t df_with_costs.at[idx, \"total_cost\"] = input_cost + output_cost\r\n 141\t \r\n 142\t except (TypeError, KeyError, IndexError) as e:\r\n 143\t print(f\"Cost calculation error for model {model} at row {idx}: {e}\")\r\n 144\t \r\n 145\t return df_with_costs\r\n 146\t\r\n 147\tdef parse_metric_details(metric_details_str):\r\n 148\t \"\"\"Safely parse JSON string from Metric_details column.\"\"\"\r\n 149\t if pd.isna(metric_details_str) or not metric_details_str:\r\n 150\t return {}\r\n 151\t try:\r\n 152\t return json.loads(metric_details_str.replace(\"'\", '\"'))\r\n 153\t except (json.JSONDecodeError, TypeError):\r\n 154\t return {}\r\n 155\t\r\n 156\tdef calculate_failure_analysis_data(df):\r\n 157\t \"\"\"Calculate failure counts by model and failure type.\"\"\"\r\n 158\t failure_series = [\r\n 159\t {\"name\": \"Invalid Diagram\", \"column\": \"Score_MermaidDiagramValid\", \"condition\": \"== 0\"},\r\n 160\t {\"name\": \"MCP Tool Failure\", \"column\": \"Score_UsedBothMCPTools\", \"condition\": \"< 1\"},\r\n 161\t {\"name\": \"Usage Limit Exceeded\", \"column\": \"Score_UsageLimitNotExceeded\", \"condition\": \"== 0\"},\r\n 162\t ]\r\n 163\t \r\n 164\t models = sorted(df[\"Model\"].unique())\r\n 165\t failure_data = []\r\n 166\t \r\n 167\t for model in models:\r\n 168\t model_data = df[df[\"Model\"] == model]\r\n 169\t failure_counts = {\"Model\": model}\r\n 170\t \r\n 171\t for series in failure_series:\r\n 172\t condition_str = f\"`{series['column']}` {series['condition']}\"\r\n 173\t count = model_data.eval(condition_str).sum()\r\n 174\t failure_counts[series[\"name\"]] = int(count)\r\n 175\t \r\n 176\t failure_data.append(failure_counts)\r\n 177\t \r\n 178\t return failure_data\r\n 179\t\r\n 180\tdef process_csv_for_static_site(csv_path):\r\n","/bin/bash -lc 'nl -ba agents_mcp_usage/evaluations/mermaid_evals/scripts/preprocess_merbench_data.py | sed -n '\"'\"'1,180p'\"'\"''",[1069],"3431",{"_733":1070,"_297":1071,"_216":1072,"_90":751},"nl -ba agents_mcp_usage/evaluations/mermaid_evals/scripts/preprocess_merbench_data.py | sed -n '1,180p'","preprocess_merbench_data.py","agents_mcp_usage/evaluations/mermaid_evals/scripts/preprocess_merbench_data.py",{"_670":1074,"_662":663,"_672":81},{"_731":-5,"_733":1066,"_735":1075,"_737":738,"_739":-5,"_741":-5,"_41":1060,"_742":1068,"_744":745,"_667":755,"_90":746},[1076],{"_733":1070,"_297":1071,"_216":1072,"_90":751},{"_670":1078,"_662":663,"_672":81},{"_759":1079,"_722":1060,"_41":-5,"_297":761,"_762":-5,"_90":763},"{\"cmd\":\"nl -ba agents_mcp_usage/evaluations/mermaid_evals/scripts/preprocess_merbench_data.py | sed -n '1,180p'\"}",{"_670":1081,"_662":663,"_672":81},{"_673":-5,"_691":1082,"_41":1083,"_694":1084,"_90":696},"gAAAAABpaAjYDret5EqvEOpPx6Hb0WgQxvFWiR598ntfj6GY3rL5vZMScXJNNALYM7TsjCqxuM2RPNJhCdsBcyQBe3i4O0PKjBJ-eVGrC82mgqOE1u3XqIDiDa7AuoN1yPTPpRdltS-4kxxAaLURQygkr5inWo3Zf_iw2mZfudpVtN3l042GxR4apz79qxHFTM0RPKo_VDOahcRANVp02clgYhVtGd8WAYfPQ-I4OC2a-sGG4moVEyW6Bi_gS7zzxO-q72D-gGJZskNGHNG4VIJwRfiy06QdFOLZCb8Hx0Pq9iWNwzpUrhCD3zTSRZmA0uKWQwm4cf2BGn2RJHDpgzQ4PFKwS-bFV4drhtqJCuDIn2ZeqpGiehUR7FP1Fm0M9dvE2YsdrcLNJUJ5ZDK8Qgy34Vy4eoPMCZXPKWxbsgJusf3ASooIXI3lM9zT9L75oUVWJhcw6oVLFDhCKGCEaBxB5_uqhSXIogDlmwhI7r69pZt4a0qTiDpYD-13qp6WzSjhLRhG5V4p-g5_lpLYqfV8QxCzSWlASjTefdIl-zavtphFgamwVbZX4bs5oVr7INkpNJQPdeG5H62sE_tTU0fMKilmyCXt8USjXCsOnzCavKcScpbvdAJ2MciFZyKeV-Do9znjAePFIPTYUQLsK-HlnEfYY9bqR2-59NX0u25tpdA5lFegNpK55DkVyRBNcwVh-AV1mr8ASJXaMdCu5GDaqGnJQXOZp7UX-WtCEMbdqqOYE6w6F6fv0Z4OkLK-mvfHkXcHKAogIp6ma6S9x8DHTpVgvS_FNoycL1JsFW_vUxHyM7vJMTX00xQ3jEfFrYocS9osI_kMFDutupe5fReampjgWE-id_3iOG4KbfgbMtpd1HqLIAjgt52qezsUzJXv5RS9t_XABRz_G8GtXMEavUi7w0E5Kga-Qdr3rOdrwyOLtqf1hR5PStrN1mw4lbDgeBFRz__AWpAEqBY3oI7mkgYsqHKzriJQb6cTjA05CNxi5JOETbsIWkZkwmTV-WEkWQLVPMmE","legacy_responseItem_reasoning_44",[1085],{"_45":1086,"_90":700},"**Planning preprocessing for normalization and costs**\n\nI'm preparing script lines to handle normalization and cost calculations, planning to cite normalization processes for OpenAI and OpenRouter responses using natural language.",{"_670":1088,"_662":663,"_672":81},{"_673":1089,"_41":1090,"_694":1091,"_90":696},[1086],"legacy_reasoning_43",[],{"_670":1093,"_662":663,"_672":81},{"_673":1094,"_41":1090,"_694":1095,"_90":696},[],[],{"_670":1097,"_662":663,"_672":81},{"_722":1098,"_724":1099,"_90":726},"call_rzPKhe7ersfx6r4gBxp1AsUs",{"_408":1100,"_728":-5},"Chunk ID: 7d2971\nWall time: 0.0517 seconds\nProcess exited with code 0\nOriginal token count: 2388\nOutput:\n 1\t#!/usr/bin/env python3\r\n 2\t\"\"\"Merge multiple Merbench processed JSON result files.\r\n 3\t\r\n 4\tThis script combines one or more JSON files produced by\r\n 5\t`preprocess_merbench_data.py`, optionally deduplicates runs for the\r\n 6\tsame (Model, Case) combination using different strategies, and then\r\n 7\trecomputes all aggregate sections from the merged raw data.\r\n 8\t\r\n 9\tThe output JSON has the same schema as a single processed file:\r\n 10\t- stats\r\n 11\t- leaderboard\r\n 12\t- pareto_data\r\n 13\t- test_groups_data\r\n 14\t- failure_analysis_data\r\n 15\t- cost_breakdown_data\r\n 16\t- raw_data\r\n 17\t- config\r\n 18\t\r\n 19\tUsage examples (as documented in README):\r\n 20\t\r\n 21\t python scripts/merge_benchmark_results.py \\\r\n 22\t -i file1.json file2.json \\\r\n 23\t -o merged.json\r\n 24\t\r\n 25\t python scripts/merge_benchmark_results.py \\\r\n 26\t -i file1.json file2.json \\\r\n 27\t -o merged.json \\\r\n 28\t --dedup keep-first\r\n 29\t\r\n 30\t python scripts/merge_benchmark_results.py \\\r\n 31\t -i file1.json file2.json \\\r\n 32\t -o merged.json \\\r\n 33\t --report merge_report.json \\\r\n 34\t --verbose\r\n 35\t\"\"\"\r\n 36\t\r\n 37\tfrom __future__ import annotations\r\n 38\t\r\n 39\timport argparse\r\n 40\timport json\r\n 41\timport sys\r\n 42\tfrom dataclasses import dataclass\r\n 43\tfrom datetime import datetime\r\n 44\tfrom pathlib import Path\r\n 45\tfrom typing import Any, Dict, Iterable, List, Literal, Optional\r\n 46\t\r\n 47\timport pandas as pd\r\n 48\t\r\n 49\t# Add parent directory to path to import project modules (mirrors preprocess script)\r\n 50\tsys.path.append(str(Path(__file__).parent.parent))\r\n 51\t\r\n 52\tfrom agents_mcp_usage.utils import get_project_root\r\n 53\t\r\n 54\t\r\n 55\tDedupStrategy = Literal[\"keep-all\", \"keep-first\", \"keep-last\", \"average\"]\r\n 56\t\r\n 57\t\r\n 58\t@dataclass\r\n 59\tclass InputFileSummary:\r\n 60\t path: str\r\n 61\t total_runs: int\r\n 62\t models: List[str]\r\n 63\t test_cases: List[str]\r\n 64\t\r\n 65\t\r\n 66\tdef load_json_file(path: Path, verbose: bool = False) -> Dict[str, Any]:\r\n 67\t if verbose:\r\n 68\t print(f\"Loading {path}...\")\r\n 69\t with path.open(\"r\", encoding=\"utf-8\") as f:\r\n 70\t return json.load(f)\r\n 71\t\r\n 72\t\r\n 73\tdef merge_raw_data(\r\n 74\t datasets: Iterable[Dict[str, Any]],\r\n 75\t dedup_strategy: DedupStrategy = \"keep-all\",\r\n 76\t verbose: bool = False,\r\n 77\t) -> pd.DataFrame:\r\n 78\t \"\"\"Merge raw_data arrays from multiple processed JSON datasets.\r\n 79\t\r\n 80\t Deduplication is performed on (Model, Case) pairs when requested.\r\n 81\t \"\"\"\r\n 82\t\r\n 83\t all_rows: List[Dict[str, Any]] = []\r\n 84\t for data in datasets:\r\n 85\t raw = data.get(\"raw_data\", [])\r\n 86\t if not isinstance(raw, list):\r\n 87\t raise ValueError(\"Each input JSON must contain a 'raw_data' list.\")\r\n 88\t all_rows.extend(raw)\r\n 89\t\r\n 90\t if not all_rows:\r\n 91\t # Return empty DataFrame with no rows\r\n 92\t return pd.DataFrame()\r\n 93\t\r\n 94\t df_all = pd.DataFrame(all_rows)\r\n 95\t\r\n 96\t # First, drop exact duplicate rows across all columns to avoid double-counting\r\n 97\t # when the same processed JSON is merged multiple times.\r\n 98\t df_all = df_all.drop_duplicates().reset_index(drop=True)\r\n 99\t\r\n 100\t # Ensure key columns exist\r\n 101\t for col in [\"Model\", \"Case\"]:\r\n 102\t if col not in df_all.columns:\r\n 103\t raise KeyError(f\"Expected column '{col}' to be present in raw_data\")\r\n 104\t\r\n 105\t if dedup_strategy == \"keep-all\":\r\n 106\t if verbose:\r\n 107\t print(\"Deduplication strategy 'keep-all': keeping all unique rows (no per-(Model, Case) deduplication).\")\r\n 108\t return df_all.reset_index(drop=True)\r\n 109\t\r\n 110\t key_cols = [\"Model\", \"Case\"]\r\n 111\t\r\n 112\t if dedup_strategy in {\"keep-first\", \"keep-last\"}:\r\n 113\t keep_arg = \"first\" if dedup_strategy == \"keep-first\" else \"last\"\r\n 114\t if verbose:\r\n 115\t print(f\"Deduplication strategy '{dedup_strategy}': dropping duplicates, keeping {keep_arg}.\")\r\n 116\t df_dedup = df_all.drop_duplicates(subset=key_cols, keep=keep_arg).reset_index(drop=True)\r\n 117\t return df_dedup\r\n 118\t\r\n 119\t if dedup_strategy == \"average\":\r\n 120\t if verbose:\r\n 121\t print(\"Deduplication strategy 'average': averaging numeric fields for duplicate (Model, Case) pairs.\")\r\n 122\t\r\n 123\t # Identify numeric vs non-numeric columns\r\n 124\t numeric_cols = df_all.select_dtypes(include=\"number\").columns.tolist()\r\n 125\t\r\n 126\t agg_dict = {}\r\n 127\t for col in df_all.columns:\r\n 128\t if col in key_cols:\r\n 129\t # Key columns are included in groupby, no aggregation spec needed\r\n 130\t continue\r\n 131\t if col in numeric_cols:\r\n 132\t agg_dict[col] = \"mean\"\r\n 133\t else:\r\n 134\t agg_dict[col] = \"first\"\r\n 135\t\r\n 136\t grouped = df_all.groupby(key_cols, as_index=False).agg(agg_dict)\r\n 137\t return grouped.reset_index(drop=True)\r\n 138\t\r\n 139\t raise ValueError(f\"Unknown deduplication strategy: {dedup_strategy}\")\r\n 140\t\r\n 141\t\r\n 142\tdef calculate_failure_analysis_data(df: pd.DataFrame) -> List[Dict[str, Any]]:\r\n 143\t \"\"\"Calculate failure counts by model and failure type.\r\n 144\t\r\n 145\t This mirrors the logic in preprocess_merbench_data.calculate_failure_analysis_data,\r\n 146\t but operates on the merged DataFrame.\r\n 147\t \"\"\"\r\n 148\t\r\n 149\t failure_series = [\r\n 150\t {\"name\": \"Invalid Diagram\", \"column\": \"Score_MermaidDiagramValid\", \"condition\": \"== 0\"},\r\n 151\t {\"name\": \"MCP Tool Failure\", \"column\": \"Score_UsedBothMCPTools\", \"condition\": \"< 1\"},\r\n 152\t {\"name\": \"Usage Limit Exceeded\", \"column\": \"Score_UsageLimitNotExceeded\", \"condition\": \"== 0\"},\r\n 153\t ]\r\n 154\t\r\n 155\t if df.empty:\r\n 156\t return []\r\n 157\t\r\n 158\t models = sorted(df[\"Model\"].unique())\r\n 159\t failure_data: List[Dict[str, Any]] = []\r\n 160\t\r\n 161\t for model in models:\r\n 162\t model_data = df[df[\"Model\"] == model]\r\n 163\t failure_counts: Dict[str, Any] = {\"Model\": model}\r\n 164\t\r\n 165\t for series in failure_series:\r\n 166\t column = series[\"column\"]\r\n 167\t if column not in model_data.columns:\r\n 168\t # If a score column is missing entirely, treat as zero failures\r\n 169\t failure_counts[series[\"name\"]] = 0\r\n 170\t continue\r\n 171\t condition_str = f\"`{column}` {series['condition']}\"\r\n 172\t count = model_data.eval(condition_str).sum()\r\n 173\t failure_counts[series[\"name\"]] = int(count)\r\n 174\t\r\n 175\t failure_data.append(failure_counts)\r\n 176\t\r\n 177\t return failure_data\r\n 178\t\r\n 179\t\r\n 180\tdef build_aggregates(df: pd.DataFrame, config: Dict[str, Any]) -> Dict[str, Any]:\r\n 181\t \"\"\"Build all aggregate sections from merged raw_data DataFrame.\r\n 182\t\r\n 183\t This mirrors the logic in preprocess_merbench_data.process_csv_for_static_site,\r\n 184\t but assumes costs and tokens are already computed in raw_data.\r\n 185\t \"\"\"\r\n 186\t\r\n 187\t if df.empty:\r\n 188\t # Return an empty-but-well-formed structure\r\n 189\t stats = {\r\n 190\t \"total_runs\": 0,\r\n 191\t \"models_evaluated\": 0,\r\n 192\t \"test_cases\": 0,\r\n 193\t \"test_groups\": [],\r\n 194\t \"providers\": [],\r\n 195\t \"models\": [],\r\n 196\t \"total_cost\": 0.0,\r\n 197\t \"avg_cost_per_run\": 0.0,\r\n 198\t }\r\n 199\t\r\n 200\t return {\r\n 201\t \"stats\": stats,\r\n 202\t \"leaderboard\": [],\r\n 203\t \"pareto_data\": [],\r\n 204\t \"test_groups_data\": [],\r\n 205\t \"failure_analysis_data\": [],\r\n 206\t \"cost_breakdown_data\": [],\r\n 207\t \"raw_data\": [],\r\n 208\t \"config\": config or {},\r\n 209\t }\r\n 210\t\r\n 211\t # Ensure expected columns exist; fill missing numeric columns with 0\r\n 212\t numeric_defaults = [\r\n 213\t \"Duration\",\r\n 214\t \"Score_MermaidDiagramValid\",\r\n 215\t \"Score_UsageLimitNotExceeded\",\r\n 216\t \"Score_UsedBothMCPTools\",\r\n 217\t \"total_tokens\",\r\n 218\t \"Metric_request_tokens\",\r\n 219\t \"Metric_response_tokens\",\r\n 220\t \"total_cost\",\r\n 221\t \"input_cost\",\r\n 222\t \"output_cost\",\r\n 223\t ]\r\n 224\t\r\n 225\t for col in numeric_defaults:\r\n 226\t if col not in df.columns:\r\n 227\t df[col] = 0.0\r\n 228\t\r\n 229\t # Fill NaN with 0 for numeric columns\r\n 230\t numeric_columns = df.select_dtypes(include=\"number\").columns\r\n 231\t df[numeric_columns] = df[numeric_columns].fillna(0)\r\n 232\t\r\n 233\t # Derive Success_Rate as in preprocess script\r\n 234\t df[\"Success_Rate\"] = df[\"Score_MermaidDiagramValid\"] * 100\r\n 235\t\r\n 236\t # Leaderboard data (group by Model)\r\n 237\t leaderboard = df.groupby(\"Model\").agg({\r\n 238\t \"Success_Rate\": \"mean\",\r\n 239\t \"Duration\": \"mean\",\r\n 240\t \"total_tokens\": \"mean\",\r\n",{"_670":1102,"_662":663,"_672":81},{"_731":1103,"_733":1104,"_735":1105,"_737":738,"_739":740,"_741":31,"_41":1098,"_742":1106,"_744":745,"_667":111,"_90":746}," 1\t#!/usr/bin/env python3\r\n 2\t\"\"\"Merge multiple Merbench processed JSON result files.\r\n 3\t\r\n 4\tThis script combines one or more JSON files produced by\r\n 5\t`preprocess_merbench_data.py`, optionally deduplicates runs for the\r\n 6\tsame (Model, Case) combination using different strategies, and then\r\n 7\trecomputes all aggregate sections from the merged raw data.\r\n 8\t\r\n 9\tThe output JSON has the same schema as a single processed file:\r\n 10\t- stats\r\n 11\t- leaderboard\r\n 12\t- pareto_data\r\n 13\t- test_groups_data\r\n 14\t- failure_analysis_data\r\n 15\t- cost_breakdown_data\r\n 16\t- raw_data\r\n 17\t- config\r\n 18\t\r\n 19\tUsage examples (as documented in README):\r\n 20\t\r\n 21\t python scripts/merge_benchmark_results.py \\\r\n 22\t -i file1.json file2.json \\\r\n 23\t -o merged.json\r\n 24\t\r\n 25\t python scripts/merge_benchmark_results.py \\\r\n 26\t -i file1.json file2.json \\\r\n 27\t -o merged.json \\\r\n 28\t --dedup keep-first\r\n 29\t\r\n 30\t python scripts/merge_benchmark_results.py \\\r\n 31\t -i file1.json file2.json \\\r\n 32\t -o merged.json \\\r\n 33\t --report merge_report.json \\\r\n 34\t --verbose\r\n 35\t\"\"\"\r\n 36\t\r\n 37\tfrom __future__ import annotations\r\n 38\t\r\n 39\timport argparse\r\n 40\timport json\r\n 41\timport sys\r\n 42\tfrom dataclasses import dataclass\r\n 43\tfrom datetime import datetime\r\n 44\tfrom pathlib import Path\r\n 45\tfrom typing import Any, Dict, Iterable, List, Literal, Optional\r\n 46\t\r\n 47\timport pandas as pd\r\n 48\t\r\n 49\t# Add parent directory to path to import project modules (mirrors preprocess script)\r\n 50\tsys.path.append(str(Path(__file__).parent.parent))\r\n 51\t\r\n 52\tfrom agents_mcp_usage.utils import get_project_root\r\n 53\t\r\n 54\t\r\n 55\tDedupStrategy = Literal[\"keep-all\", \"keep-first\", \"keep-last\", \"average\"]\r\n 56\t\r\n 57\t\r\n 58\t@dataclass\r\n 59\tclass InputFileSummary:\r\n 60\t path: str\r\n 61\t total_runs: int\r\n 62\t models: List[str]\r\n 63\t test_cases: List[str]\r\n 64\t\r\n 65\t\r\n 66\tdef load_json_file(path: Path, verbose: bool = False) -> Dict[str, Any]:\r\n 67\t if verbose:\r\n 68\t print(f\"Loading {path}...\")\r\n 69\t with path.open(\"r\", encoding=\"utf-8\") as f:\r\n 70\t return json.load(f)\r\n 71\t\r\n 72\t\r\n 73\tdef merge_raw_data(\r\n 74\t datasets: Iterable[Dict[str, Any]],\r\n 75\t dedup_strategy: DedupStrategy = \"keep-all\",\r\n 76\t verbose: bool = False,\r\n 77\t) -> pd.DataFrame:\r\n 78\t \"\"\"Merge raw_data arrays from multiple processed JSON datasets.\r\n 79\t\r\n 80\t Deduplication is performed on (Model, Case) pairs when requested.\r\n 81\t \"\"\"\r\n 82\t\r\n 83\t all_rows: List[Dict[str, Any]] = []\r\n 84\t for data in datasets:\r\n 85\t raw = data.get(\"raw_data\", [])\r\n 86\t if not isinstance(raw, list):\r\n 87\t raise ValueError(\"Each input JSON must contain a 'raw_data' list.\")\r\n 88\t all_rows.extend(raw)\r\n 89\t\r\n 90\t if not all_rows:\r\n 91\t # Return empty DataFrame with no rows\r\n 92\t return pd.DataFrame()\r\n 93\t\r\n 94\t df_all = pd.DataFrame(all_rows)\r\n 95\t\r\n 96\t # First, drop exact duplicate rows across all columns to avoid double-counting\r\n 97\t # when the same processed JSON is merged multiple times.\r\n 98\t df_all = df_all.drop_duplicates().reset_index(drop=True)\r\n 99\t\r\n 100\t # Ensure key columns exist\r\n 101\t for col in [\"Model\", \"Case\"]:\r\n 102\t if col not in df_all.columns:\r\n 103\t raise KeyError(f\"Expected column '{col}' to be present in raw_data\")\r\n 104\t\r\n 105\t if dedup_strategy == \"keep-all\":\r\n 106\t if verbose:\r\n 107\t print(\"Deduplication strategy 'keep-all': keeping all unique rows (no per-(Model, Case) deduplication).\")\r\n 108\t return df_all.reset_index(drop=True)\r\n 109\t\r\n 110\t key_cols = [\"Model\", \"Case\"]\r\n 111\t\r\n 112\t if dedup_strategy in {\"keep-first\", \"keep-last\"}:\r\n 113\t keep_arg = \"first\" if dedup_strategy == \"keep-first\" else \"last\"\r\n 114\t if verbose:\r\n 115\t print(f\"Deduplication strategy '{dedup_strategy}': dropping duplicates, keeping {keep_arg}.\")\r\n 116\t df_dedup = df_all.drop_duplicates(subset=key_cols, keep=keep_arg).reset_index(drop=True)\r\n 117\t return df_dedup\r\n 118\t\r\n 119\t if dedup_strategy == \"average\":\r\n 120\t if verbose:\r\n 121\t print(\"Deduplication strategy 'average': averaging numeric fields for duplicate (Model, Case) pairs.\")\r\n 122\t\r\n 123\t # Identify numeric vs non-numeric columns\r\n 124\t numeric_cols = df_all.select_dtypes(include=\"number\").columns.tolist()\r\n 125\t\r\n 126\t agg_dict = {}\r\n 127\t for col in df_all.columns:\r\n 128\t if col in key_cols:\r\n 129\t # Key columns are included in groupby, no aggregation spec needed\r\n 130\t continue\r\n 131\t if col in numeric_cols:\r\n 132\t agg_dict[col] = \"mean\"\r\n 133\t else:\r\n 134\t agg_dict[col] = \"first\"\r\n 135\t\r\n 136\t grouped = df_all.groupby(key_cols, as_index=False).agg(agg_dict)\r\n 137\t return grouped.reset_index(drop=True)\r\n 138\t\r\n 139\t raise ValueError(f\"Unknown deduplication strategy: {dedup_strategy}\")\r\n 140\t\r\n 141\t\r\n 142\tdef calculate_failure_analysis_data(df: pd.DataFrame) -> List[Dict[str, Any]]:\r\n 143\t \"\"\"Calculate failure counts by model and failure type.\r\n 144\t\r\n 145\t This mirrors the logic in preprocess_merbench_data.calculate_failure_analysis_data,\r\n 146\t but operates on the merged DataFrame.\r\n 147\t \"\"\"\r\n 148\t\r\n 149\t failure_series = [\r\n 150\t {\"name\": \"Invalid Diagram\", \"column\": \"Score_MermaidDiagramValid\", \"condition\": \"== 0\"},\r\n 151\t {\"name\": \"MCP Tool Failure\", \"column\": \"Score_UsedBothMCPTools\", \"condition\": \"< 1\"},\r\n 152\t {\"name\": \"Usage Limit Exceeded\", \"column\": \"Score_UsageLimitNotExceeded\", \"condition\": \"== 0\"},\r\n 153\t ]\r\n 154\t\r\n 155\t if df.empty:\r\n 156\t return []\r\n 157\t\r\n 158\t models = sorted(df[\"Model\"].unique())\r\n 159\t failure_data: List[Dict[str, Any]] = []\r\n 160\t\r\n 161\t for model in models:\r\n 162\t model_data = df[df[\"Model\"] == model]\r\n 163\t failure_counts: Dict[str, Any] = {\"Model\": model}\r\n 164\t\r\n 165\t for series in failure_series:\r\n 166\t column = series[\"column\"]\r\n 167\t if column not in model_data.columns:\r\n 168\t # If a score column is missing entirely, treat as zero failures\r\n 169\t failure_counts[series[\"name\"]] = 0\r\n 170\t continue\r\n 171\t condition_str = f\"`{column}` {series['condition']}\"\r\n 172\t count = model_data.eval(condition_str).sum()\r\n 173\t failure_counts[series[\"name\"]] = int(count)\r\n 174\t\r\n 175\t failure_data.append(failure_counts)\r\n 176\t\r\n 177\t return failure_data\r\n 178\t\r\n 179\t\r\n 180\tdef build_aggregates(df: pd.DataFrame, config: Dict[str, Any]) -> Dict[str, Any]:\r\n 181\t \"\"\"Build all aggregate sections from merged raw_data DataFrame.\r\n 182\t\r\n 183\t This mirrors the logic in preprocess_merbench_data.process_csv_for_static_site,\r\n 184\t but assumes costs and tokens are already computed in raw_data.\r\n 185\t \"\"\"\r\n 186\t\r\n 187\t if df.empty:\r\n 188\t # Return an empty-but-well-formed structure\r\n 189\t stats = {\r\n 190\t \"total_runs\": 0,\r\n 191\t \"models_evaluated\": 0,\r\n 192\t \"test_cases\": 0,\r\n 193\t \"test_groups\": [],\r\n 194\t \"providers\": [],\r\n 195\t \"models\": [],\r\n 196\t \"total_cost\": 0.0,\r\n 197\t \"avg_cost_per_run\": 0.0,\r\n 198\t }\r\n 199\t\r\n 200\t return {\r\n 201\t \"stats\": stats,\r\n 202\t \"leaderboard\": [],\r\n 203\t \"pareto_data\": [],\r\n 204\t \"test_groups_data\": [],\r\n 205\t \"failure_analysis_data\": [],\r\n 206\t \"cost_breakdown_data\": [],\r\n 207\t \"raw_data\": [],\r\n 208\t \"config\": config or {},\r\n 209\t }\r\n 210\t\r\n 211\t # Ensure expected columns exist; fill missing numeric columns with 0\r\n 212\t numeric_defaults = [\r\n 213\t \"Duration\",\r\n 214\t \"Score_MermaidDiagramValid\",\r\n 215\t \"Score_UsageLimitNotExceeded\",\r\n 216\t \"Score_UsedBothMCPTools\",\r\n 217\t \"total_tokens\",\r\n 218\t \"Metric_request_tokens\",\r\n 219\t \"Metric_response_tokens\",\r\n 220\t \"total_cost\",\r\n 221\t \"input_cost\",\r\n 222\t \"output_cost\",\r\n 223\t ]\r\n 224\t\r\n 225\t for col in numeric_defaults:\r\n 226\t if col not in df.columns:\r\n 227\t df[col] = 0.0\r\n 228\t\r\n 229\t # Fill NaN with 0 for numeric columns\r\n 230\t numeric_columns = df.select_dtypes(include=\"number\").columns\r\n 231\t df[numeric_columns] = df[numeric_columns].fillna(0)\r\n 232\t\r\n 233\t # Derive Success_Rate as in preprocess script\r\n 234\t df[\"Success_Rate\"] = df[\"Score_MermaidDiagramValid\"] * 100\r\n 235\t\r\n 236\t # Leaderboard data (group by Model)\r\n 237\t leaderboard = df.groupby(\"Model\").agg({\r\n 238\t \"Success_Rate\": \"mean\",\r\n 239\t \"Duration\": \"mean\",\r\n 240\t \"total_tokens\": \"mean\",\r\n","/bin/bash -lc 'nl -ba agents_mcp_usage/evaluations/mermaid_evals/scripts/merge_benchmark_results.py | sed -n '\"'\"'1,240p'\"'\"''",[1107],"66007",{"_733":1108,"_297":1109,"_216":1110,"_90":751},"nl -ba agents_mcp_usage/evaluations/mermaid_evals/scripts/merge_benchmark_results.py | sed -n '1,240p'","merge_benchmark_results.py","agents_mcp_usage/evaluations/mermaid_evals/scripts/merge_benchmark_results.py",{"_670":1112,"_662":663,"_672":81},{"_731":-5,"_733":1104,"_735":1113,"_737":738,"_739":-5,"_741":-5,"_41":1098,"_742":1106,"_744":745,"_667":755,"_90":746},[1114],{"_733":1108,"_297":1109,"_216":1110,"_90":751},{"_670":1116,"_662":663,"_672":81},{"_759":1117,"_722":1098,"_41":-5,"_297":761,"_762":-5,"_90":763},"{\"cmd\":\"nl -ba agents_mcp_usage/evaluations/mermaid_evals/scripts/merge_benchmark_results.py | sed -n '1,240p'\"}",{"_670":1119,"_662":663,"_672":81},{"_673":-5,"_691":1120,"_41":1121,"_694":1122,"_90":696},"gAAAAABpaAjUQWI2EB95MvltO3g3LEI3S6Zi4kY7Z0Zwcc2EpuuH_Eqx-b5t3diYd_Kx9CZ_NEpNtXO8Q0n22k75e3wNBJ95jKBfelcI3UMLk0FuArB5_LZNnS6SsPn9iHM-6wKhMvre3VPrKo9BqKHdabTjFb-lDo0qjW1saYJyhUYb6NlKAYHZvc7pLLh1wsOyhZSsQbfAK5s1j3TesF50axb8S2cbusNhG8Vg5WAdlmXpmimbk7hGBxFqTIojSW6gy-8CYQ13wEFgHkcC7lCanOO17cFCI2Ia9bZsmNK2_DyRNJFrz5CHDAUlfScB4-qwn62TMZJqkagM_Mo1Zaru4hoC3bDevPfmHfl0NMSlSsEEXS2fXC7xWkZMxKns2iaJw4qYkNmkG5z0Yj26BpsSdO41H7_U2UDec7RCMbj5bMNxZ6EWVmErps4dRx0laFo7-g3sHAvanlxlx6f4HR6g-uLSXkvQ5ZhSHDptoaTmexwSWsmYC8W6qJwUAW_XB25zpTgxxH-tzRvvdmdJ-1TtbZw0B5FhKXZ4thaFVI3hynnUIXgRMcHuc-q50987JHJIRRQzd-U06Y166FBXY-lri7wC5s2fjWLJA_4ixibR0ozFwm82bSXXN1IEwl5Bif7ptfaBjxuvLHJZU_xBey_hqAJD8tMuLt0Dorz-ua8L-Bzh4NAR81wgJHaWIdP6Ch2kOvVOxtuC4giLhtkbWN97EqM_FVi-rgaXuHOC3B_dimd4GiNIQftkk5rr7fZoKVk2Sl3r8O8ODwxWaLFZjQwoecKsBbHdUoaX6qNEJZyEdcvnUh-mpd16-nWsoXzKeCYZI4w8L27S_UO6DPl80nrw2e-aadPK-1c8aVew4BS7w3un2pdcOQ0U0XPq3p0794yysXlBCFFfLp-ZmJOaMY7XWdlc76jh3irim2zoc3mCaKyywtFnI99hcVPKE4xXPK1XG0Sipk06WDfTzagMuaaT_NeZ8ndaSJwspj82cYL36Z7pjP7OGdCtbjZ7VKARVmEHjxiY-4_WaJy-TjNbiqoO5uhZShx4IH1uwscBi7jNz3uSFuPm9Q8Yd1szP0ucDnANUme0MKQvzlw58ZI3zN_DTQosw1puRsc6H3sButrprV7mg8v74KiICnLjrFoOkC9AQNdnrF6mkIPCdK_8RSRPLLgzLiXyUyyrIWnfxHJSRIix042eohfD3hsTbVWPX5Xht8DZtO1m9jwdgMpJOf-eqh4IU-H110LGdI1WkquXDH0EFgnaJBag_dLcj4qNT1lzfPqZCGdXl1XtyzORRwzEOjexEM_zAPz2fW06qXKuctSl8o2naHPzeEftwdKjMjwJWS1q3Y0VIIIrz_B8YJJPCdkAjFlITN-rwkkJmkhK6PCmoTZp7gjcUDOImi8U5bPiu6HoZEWoE2R3KoVA0vuD333IbhAk3DDsjBN10MhVDFhIhDtQAhc4ZCQdOk8gZ_shmXRFt68ovjWDtQXsK7Sf7HByWnI7dOXASAA44KVrkHY29jkFgAUDLIpgHrbDB87F42IjhZLsYLhvWgMDjedtsi0oSn8ITAOctO92c0Yn3_Er7EGDdPQ=","legacy_responseItem_reasoning_42",[1123],{"_45":1124,"_90":700},"**Summarizing retry, limits, and new scripts**\n\nI'll highlight improved retry/backoff, request limit of 500, and retry backoff config as already noted, plus mention the new scripts for merging benchmark results and verifying retry orchestration with relevant line references.",{"_670":1126,"_662":663,"_672":81},{"_673":1127,"_41":1128,"_694":1129,"_90":696},[1124],"legacy_reasoning_41",[],{"_670":1131,"_662":663,"_672":81},{"_673":1132,"_41":1128,"_694":1133,"_90":696},[],[],{"_670":1135,"_662":663,"_672":81},{"_722":1136,"_724":1137,"_90":726},"call_IWfDHZOCdvrBFSzrJuYg6V30",{"_408":1138,"_728":-5},"Chunk ID: 953830\nWall time: 0.0514 seconds\nProcess exited with code 0\nOriginal token count: 2249\nOutput:\n 300\t\r\n 301\t Args:\r\n 302\t model: The model to use for the agent.\r\n 303\t model_settings: Optional model-specific settings.\r\n 304\t\r\n 305\t Returns:\r\n 306\t A configured Agent instance.\r\n 307\t \"\"\"\r\n 308\t if model_settings is None:\r\n 309\t model_settings = {}\r\n 310\t\r\n 311\t # Use the new model factory for all models\r\n 312\t return create_agent_with_model(\r\n 313\t model=model,\r\n 314\t mcp_servers=get_mcp_servers(),\r\n 315\t model_settings=model_settings,\r\n 316\t )\r\n 317\t\r\n 318\t\r\n 319\t# ============================================================================\r\n 320\t# Schema Definitions\r\n 321\t# ============================================================================\r\n 322\t\r\n 323\t\r\n 324\tclass MermaidInput(BaseModel):\r\n 325\t \"\"\"Input schema for mermaid diagram fixing.\"\"\"\r\n 326\t\r\n 327\t invalid_diagram: str\r\n 328\t case_name: Optional[str] = None\r\n 329\t\r\n 330\t\r\n 331\tclass MermaidOutput(BaseModel):\r\n 332\t \"\"\"Output schema for mermaid diagram fixing with comprehensive metrics.\"\"\"\r\n 333\t\r\n 334\t fixed_diagram: str\r\n 335\t failure_reason: str = \"\" # Track why a case failed\r\n 336\t metrics: Dict[str, Any] = {} # Capture LLM usage metrics\r\n 337\t tools_used: List[str] = [] # Track which MCP tools were called\r\n 338\t\r\n 339\t\r\n 340\t# ============================================================================\r\n 341\t# Debug Trace Capture\r\n 342\t# ============================================================================\r\n 343\t\r\n 344\t_SLUG_RE = re.compile(r\"[^a-zA-Z0-9]+\")\r\n 345\t\r\n 346\t\r\n 347\tdef _slugify(value: str) -> str:\r\n 348\t slug = _SLUG_RE.sub(\"_\", value).strip(\"_\")\r\n 349\t return slug or \"unknown\"\r\n 350\t\r\n 351\t\r\n 352\tdef _short_hash(value: str) -> str:\r\n 353\t return hashlib.sha256(value.encode(\"utf-8\")).hexdigest()[:12]\r\n 354\t\r\n 355\t\r\n 356\tdef _usage_limits_to_dict(usage_limits: UsageLimits) -> Dict[str, Any]:\r\n 357\t data: Dict[str, Any] = {}\r\n 358\t for key in (\r\n 359\t \"request_limit\",\r\n 360\t \"request_tokens_limit\",\r\n 361\t \"response_tokens_limit\",\r\n 362\t \"total_tokens_limit\",\r\n 363\t ):\r\n 364\t if hasattr(usage_limits, key):\r\n 365\t value = getattr(usage_limits, key)\r\n 366\t if value is not None:\r\n 367\t data[key] = value\r\n 368\t return data\r\n 369\t\r\n 370\t\r\n 371\tdef _usage_to_dict(usage: Any) -> Dict[str, Any]:\r\n 372\t \"\"\"Best-effort conversion of PydanticAI usage objects to primitives for JSON.\"\"\"\r\n 373\t details = getattr(usage, \"details\", None) or {}\r\n 374\t return {\r\n 375\t \"requests\": getattr(usage, \"requests\", None),\r\n 376\t \"request_tokens\": getattr(usage, \"request_tokens\", None),\r\n 377\t \"response_tokens\": getattr(usage, \"response_tokens\", None),\r\n 378\t \"total_tokens\": getattr(usage, \"total_tokens\", None),\r\n 379\t \"details\": details,\r\n 380\t }\r\n 381\t\r\n 382\t\r\n 383\tdef _exception_to_dict(exception: BaseException) -> Dict[str, Any]:\r\n 384\t data: Dict[str, Any] = {\r\n 385\t \"type\": type(exception).__name__,\r\n 386\t \"message\": str(exception),\r\n 387\t \"traceback\": traceback.format_exception(exception),\r\n 388\t }\r\n 389\t\r\n 390\t if isinstance(exception, ModelHTTPError):\r\n 391\t data.update(\r\n 392\t {\r\n 393\t \"status_code\": exception.status_code,\r\n 394\t \"model_name\": exception.model_name,\r\n 395\t \"body\": exception.body,\r\n 396\t }\r\n 397\t )\r\n 398\t\r\n 399\t if isinstance(exception, BaseExceptionGroup):\r\n 400\t data[\"exceptions\"] = [_exception_to_dict(e) for e in exception.exceptions]\r\n 401\t\r\n 402\t return data\r\n 403\t\r\n 404\t\r\n 405\tdef _summarize_messages(messages: Any) -> Dict[str, Any]:\r\n 406\t \"\"\"Create small derived fields to quickly spot tool loops / excessive turn usage.\r\n 407\t\r\n 408\t Accepts the parsed output of `all_messages_json()`.\r\n 409\t \"\"\"\r\n 410\t if not isinstance(messages, list):\r\n 411\t return {}\r\n 412\t\r\n 413\t summary: Dict[str, Any] = {\r\n 414\t \"message_count\": len(messages),\r\n 415\t \"request_count\": 0,\r\n 416\t \"response_count\": 0,\r\n 417\t \"tool_call_count\": 0,\r\n 418\t \"tool_return_count\": 0,\r\n 419\t \"tool_names_unique\": [],\r\n 420\t \"tool_calls_by_name\": {},\r\n 421\t \"tool_sequence\": [],\r\n 422\t }\r\n 423\t\r\n 424\t tools_seen_ordered: List[str] = []\r\n 425\t tool_calls_by_name: Dict[str, int] = {}\r\n 426\t\r\n 427\t for msg in messages:\r\n 428\t if not isinstance(msg, dict):\r\n 429\t continue\r\n 430\t\r\n 431\t kind = msg.get(\"kind\")\r\n 432\t if kind == \"request\":\r\n 433\t summary[\"request_count\"] += 1\r\n 434\t elif kind == \"response\":\r\n 435\t summary[\"response_count\"] += 1\r\n 436\t\r\n 437\t parts = msg.get(\"parts\") or []\r\n 438\t if not isinstance(parts, list):\r\n 439\t continue\r\n 440\t\r\n 441\t for part in parts:\r\n 442\t if not isinstance(part, dict):\r\n 443\t continue\r\n 444\t part_kind = part.get(\"part_kind\")\r\n 445\t tool_name = part.get(\"tool_name\")\r\n 446\t tool_call_id = part.get(\"tool_call_id\")\r\n 447\t\r\n 448\t if tool_name and tool_name not in tools_seen_ordered:\r\n 449\t tools_seen_ordered.append(tool_name)\r\n 450\t\r\n 451\t if part_kind in {\"tool-call\", \"builtin-tool-call\"}:\r\n 452\t summary[\"tool_call_count\"] += 1\r\n 453\t if tool_name:\r\n 454\t tool_calls_by_name[tool_name] = tool_calls_by_name.get(tool_name, 0) + 1\r\n 455\t summary[\"tool_sequence\"].append(\r\n 456\t {\"event\": \"call\", \"tool_name\": tool_name, \"tool_call_id\": tool_call_id}\r\n 457\t )\r\n 458\t\r\n 459\t if part_kind in {\"tool-return\", \"builtin-tool-return\"}:\r\n 460\t summary[\"tool_return_count\"] += 1\r\n 461\t summary[\"tool_sequence\"].append(\r\n 462\t {\"event\": \"return\", \"tool_name\": tool_name, \"tool_call_id\": tool_call_id}\r\n 463\t )\r\n 464\t\r\n 465\t summary[\"tool_names_unique\"] = tools_seen_ordered\r\n 466\t summary[\"tool_calls_by_name\"] = tool_calls_by_name\r\n 467\t return summary\r\n 468\t\r\n 469\t\r\n 470\t@dataclass(slots=True)\r\n 471\tclass MermaidEvalTraceContext:\r\n 472\t \"\"\"Per-case debug trace capture config passed down from evaluation runners.\"\"\"\r\n 473\t\r\n 474\t enabled: bool\r\n 475\t trace_dir: str\r\n 476\t model: str\r\n 477\t planned_run_index: Optional[int] = None # 0-based planned run index\r\n 478\t run_attempt: Optional[int] = None # 1-based attempt counter at runner-level\r\n 479\t evaluation_name: Optional[str] = None\r\n 480\t\r\n 481\t\r\n 482\tdef _build_trace_path(trace_ctx: MermaidEvalTraceContext, inputs: MermaidInput) -> str:\r\n 483\t model_slug = _slugify(trace_ctx.model)\r\n 484\t case_slug = _slugify(inputs.case_name) if inputs.case_name else f\"hash_{_short_hash(inputs.invalid_diagram)}\"\r\n 485\t run_part = (\r\n 486\t f\"run{(trace_ctx.planned_run_index + 1):03d}\"\r\n 487\t if trace_ctx.planned_run_index is not None\r\n 488\t else \"run000\"\r\n 489\t )\r\n 490\t attempt_part = (\r\n 491\t f\"attempt{trace_ctx.run_attempt:03d}\"\r\n 492\t if trace_ctx.run_attempt is not None\r\n 493\t else \"attempt000\"\r\n 494\t )\r\n 495\t\r\n 496\t model_dir = os.path.join(trace_ctx.trace_dir, model_slug)\r\n 497\t filename = f\"model={model_slug}__{run_part}__{attempt_part}__case={case_slug}.json\"\r\n 498\t return os.path.join(model_dir, filename)\r\n 499\t\r\n 500\t\r\n 501\tdef _write_trace(trace_path: str, trace_record: Dict[str, Any]) -> None:\r\n 502\t \"\"\"Best-effort writer; never raise (debug traces should not break evals).\"\"\"\r\n 503\t try:\r\n 504\t os.makedirs(os.path.dirname(trace_path), exist_ok=True)\r\n 505\t tmp_path = f\"{trace_path}.tmp\"\r\n 506\t with open(tmp_path, \"w\", encoding=\"utf-8\") as f:\r\n 507\t json.dump(trace_record, f, indent=2, ensure_ascii=False, sort_keys=False)\r\n 508\t os.replace(tmp_path, trace_path)\r\n 509\t except Exception as e: # pragma: no cover\r\n 510\t logfire.warning(\r\n 511\t \"Failed to write debug trace file\",\r\n 512\t trace_path=trace_path,\r\n 513\t error_type=type(e).__name__,\r\n 514\t error=str(e),\r\n 515\t )\r\n 516\t\r\n 517\t\r\n 518\t# ============================================================================\r\n 519\t# Custom Evaluators\r\n 520\t# ============================================================================\r\n",{"_670":1140,"_662":663,"_672":81},{"_731":1141,"_733":1142,"_735":1143,"_737":738,"_739":740,"_741":31,"_41":1136,"_742":1144,"_744":745,"_667":111,"_90":746}," 300\t\r\n 301\t Args:\r\n 302\t model: The model to use for the agent.\r\n 303\t model_settings: Optional model-specific settings.\r\n 304\t\r\n 305\t Returns:\r\n 306\t A configured Agent instance.\r\n 307\t \"\"\"\r\n 308\t if model_settings is None:\r\n 309\t model_settings = {}\r\n 310\t\r\n 311\t # Use the new model factory for all models\r\n 312\t return create_agent_with_model(\r\n 313\t model=model,\r\n 314\t mcp_servers=get_mcp_servers(),\r\n 315\t model_settings=model_settings,\r\n 316\t )\r\n 317\t\r\n 318\t\r\n 319\t# ============================================================================\r\n 320\t# Schema Definitions\r\n 321\t# ============================================================================\r\n 322\t\r\n 323\t\r\n 324\tclass MermaidInput(BaseModel):\r\n 325\t \"\"\"Input schema for mermaid diagram fixing.\"\"\"\r\n 326\t\r\n 327\t invalid_diagram: str\r\n 328\t case_name: Optional[str] = None\r\n 329\t\r\n 330\t\r\n 331\tclass MermaidOutput(BaseModel):\r\n 332\t \"\"\"Output schema for mermaid diagram fixing with comprehensive metrics.\"\"\"\r\n 333\t\r\n 334\t fixed_diagram: str\r\n 335\t failure_reason: str = \"\" # Track why a case failed\r\n 336\t metrics: Dict[str, Any] = {} # Capture LLM usage metrics\r\n 337\t tools_used: List[str] = [] # Track which MCP tools were called\r\n 338\t\r\n 339\t\r\n 340\t# ============================================================================\r\n 341\t# Debug Trace Capture\r\n 342\t# ============================================================================\r\n 343\t\r\n 344\t_SLUG_RE = re.compile(r\"[^a-zA-Z0-9]+\")\r\n 345\t\r\n 346\t\r\n 347\tdef _slugify(value: str) -> str:\r\n 348\t slug = _SLUG_RE.sub(\"_\", value).strip(\"_\")\r\n 349\t return slug or \"unknown\"\r\n 350\t\r\n 351\t\r\n 352\tdef _short_hash(value: str) -> str:\r\n 353\t return hashlib.sha256(value.encode(\"utf-8\")).hexdigest()[:12]\r\n 354\t\r\n 355\t\r\n 356\tdef _usage_limits_to_dict(usage_limits: UsageLimits) -> Dict[str, Any]:\r\n 357\t data: Dict[str, Any] = {}\r\n 358\t for key in (\r\n 359\t \"request_limit\",\r\n 360\t \"request_tokens_limit\",\r\n 361\t \"response_tokens_limit\",\r\n 362\t \"total_tokens_limit\",\r\n 363\t ):\r\n 364\t if hasattr(usage_limits, key):\r\n 365\t value = getattr(usage_limits, key)\r\n 366\t if value is not None:\r\n 367\t data[key] = value\r\n 368\t return data\r\n 369\t\r\n 370\t\r\n 371\tdef _usage_to_dict(usage: Any) -> Dict[str, Any]:\r\n 372\t \"\"\"Best-effort conversion of PydanticAI usage objects to primitives for JSON.\"\"\"\r\n 373\t details = getattr(usage, \"details\", None) or {}\r\n 374\t return {\r\n 375\t \"requests\": getattr(usage, \"requests\", None),\r\n 376\t \"request_tokens\": getattr(usage, \"request_tokens\", None),\r\n 377\t \"response_tokens\": getattr(usage, \"response_tokens\", None),\r\n 378\t \"total_tokens\": getattr(usage, \"total_tokens\", None),\r\n 379\t \"details\": details,\r\n 380\t }\r\n 381\t\r\n 382\t\r\n 383\tdef _exception_to_dict(exception: BaseException) -> Dict[str, Any]:\r\n 384\t data: Dict[str, Any] = {\r\n 385\t \"type\": type(exception).__name__,\r\n 386\t \"message\": str(exception),\r\n 387\t \"traceback\": traceback.format_exception(exception),\r\n 388\t }\r\n 389\t\r\n 390\t if isinstance(exception, ModelHTTPError):\r\n 391\t data.update(\r\n 392\t {\r\n 393\t \"status_code\": exception.status_code,\r\n 394\t \"model_name\": exception.model_name,\r\n 395\t \"body\": exception.body,\r\n 396\t }\r\n 397\t )\r\n 398\t\r\n 399\t if isinstance(exception, BaseExceptionGroup):\r\n 400\t data[\"exceptions\"] = [_exception_to_dict(e) for e in exception.exceptions]\r\n 401\t\r\n 402\t return data\r\n 403\t\r\n 404\t\r\n 405\tdef _summarize_messages(messages: Any) -> Dict[str, Any]:\r\n 406\t \"\"\"Create small derived fields to quickly spot tool loops / excessive turn usage.\r\n 407\t\r\n 408\t Accepts the parsed output of `all_messages_json()`.\r\n 409\t \"\"\"\r\n 410\t if not isinstance(messages, list):\r\n 411\t return {}\r\n 412\t\r\n 413\t summary: Dict[str, Any] = {\r\n 414\t \"message_count\": len(messages),\r\n 415\t \"request_count\": 0,\r\n 416\t \"response_count\": 0,\r\n 417\t \"tool_call_count\": 0,\r\n 418\t \"tool_return_count\": 0,\r\n 419\t \"tool_names_unique\": [],\r\n 420\t \"tool_calls_by_name\": {},\r\n 421\t \"tool_sequence\": [],\r\n 422\t }\r\n 423\t\r\n 424\t tools_seen_ordered: List[str] = []\r\n 425\t tool_calls_by_name: Dict[str, int] = {}\r\n 426\t\r\n 427\t for msg in messages:\r\n 428\t if not isinstance(msg, dict):\r\n 429\t continue\r\n 430\t\r\n 431\t kind = msg.get(\"kind\")\r\n 432\t if kind == \"request\":\r\n 433\t summary[\"request_count\"] += 1\r\n 434\t elif kind == \"response\":\r\n 435\t summary[\"response_count\"] += 1\r\n 436\t\r\n 437\t parts = msg.get(\"parts\") or []\r\n 438\t if not isinstance(parts, list):\r\n 439\t continue\r\n 440\t\r\n 441\t for part in parts:\r\n 442\t if not isinstance(part, dict):\r\n 443\t continue\r\n 444\t part_kind = part.get(\"part_kind\")\r\n 445\t tool_name = part.get(\"tool_name\")\r\n 446\t tool_call_id = part.get(\"tool_call_id\")\r\n 447\t\r\n 448\t if tool_name and tool_name not in tools_seen_ordered:\r\n 449\t tools_seen_ordered.append(tool_name)\r\n 450\t\r\n 451\t if part_kind in {\"tool-call\", \"builtin-tool-call\"}:\r\n 452\t summary[\"tool_call_count\"] += 1\r\n 453\t if tool_name:\r\n 454\t tool_calls_by_name[tool_name] = tool_calls_by_name.get(tool_name, 0) + 1\r\n 455\t summary[\"tool_sequence\"].append(\r\n 456\t {\"event\": \"call\", \"tool_name\": tool_name, \"tool_call_id\": tool_call_id}\r\n 457\t )\r\n 458\t\r\n 459\t if part_kind in {\"tool-return\", \"builtin-tool-return\"}:\r\n 460\t summary[\"tool_return_count\"] += 1\r\n 461\t summary[\"tool_sequence\"].append(\r\n 462\t {\"event\": \"return\", \"tool_name\": tool_name, \"tool_call_id\": tool_call_id}\r\n 463\t )\r\n 464\t\r\n 465\t summary[\"tool_names_unique\"] = tools_seen_ordered\r\n 466\t summary[\"tool_calls_by_name\"] = tool_calls_by_name\r\n 467\t return summary\r\n 468\t\r\n 469\t\r\n 470\t@dataclass(slots=True)\r\n 471\tclass MermaidEvalTraceContext:\r\n 472\t \"\"\"Per-case debug trace capture config passed down from evaluation runners.\"\"\"\r\n 473\t\r\n 474\t enabled: bool\r\n 475\t trace_dir: str\r\n 476\t model: str\r\n 477\t planned_run_index: Optional[int] = None # 0-based planned run index\r\n 478\t run_attempt: Optional[int] = None # 1-based attempt counter at runner-level\r\n 479\t evaluation_name: Optional[str] = None\r\n 480\t\r\n 481\t\r\n 482\tdef _build_trace_path(trace_ctx: MermaidEvalTraceContext, inputs: MermaidInput) -> str:\r\n 483\t model_slug = _slugify(trace_ctx.model)\r\n 484\t case_slug = _slugify(inputs.case_name) if inputs.case_name else f\"hash_{_short_hash(inputs.invalid_diagram)}\"\r\n 485\t run_part = (\r\n 486\t f\"run{(trace_ctx.planned_run_index + 1):03d}\"\r\n 487\t if trace_ctx.planned_run_index is not None\r\n 488\t else \"run000\"\r\n 489\t )\r\n 490\t attempt_part = (\r\n 491\t f\"attempt{trace_ctx.run_attempt:03d}\"\r\n 492\t if trace_ctx.run_attempt is not None\r\n 493\t else \"attempt000\"\r\n 494\t )\r\n 495\t\r\n 496\t model_dir = os.path.join(trace_ctx.trace_dir, model_slug)\r\n 497\t filename = f\"model={model_slug}__{run_part}__{attempt_part}__case={case_slug}.json\"\r\n 498\t return os.path.join(model_dir, filename)\r\n 499\t\r\n 500\t\r\n 501\tdef _write_trace(trace_path: str, trace_record: Dict[str, Any]) -> None:\r\n 502\t \"\"\"Best-effort writer; never raise (debug traces should not break evals).\"\"\"\r\n 503\t try:\r\n 504\t os.makedirs(os.path.dirname(trace_path), exist_ok=True)\r\n 505\t tmp_path = f\"{trace_path}.tmp\"\r\n 506\t with open(tmp_path, \"w\", encoding=\"utf-8\") as f:\r\n 507\t json.dump(trace_record, f, indent=2, ensure_ascii=False, sort_keys=False)\r\n 508\t os.replace(tmp_path, trace_path)\r\n 509\t except Exception as e: # pragma: no cover\r\n 510\t logfire.warning(\r\n 511\t \"Failed to write debug trace file\",\r\n 512\t trace_path=trace_path,\r\n 513\t error_type=type(e).__name__,\r\n 514\t error=str(e),\r\n 515\t )\r\n 516\t\r\n 517\t\r\n 518\t# ============================================================================\r\n 519\t# Custom Evaluators\r\n 520\t# ============================================================================\r\n","/bin/bash -lc 'nl -ba agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py | sed -n '\"'\"'300,520p'\"'\"''",[1145],"90380",{"_733":1146,"_297":881,"_216":882,"_90":751},"nl -ba agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py | sed -n '300,520p'",{"_670":1148,"_662":663,"_672":81},{"_731":-5,"_733":1142,"_735":1149,"_737":738,"_739":-5,"_741":-5,"_41":1136,"_742":1144,"_744":745,"_667":755,"_90":746},[1150],{"_733":1146,"_297":881,"_216":882,"_90":751},{"_670":1152,"_662":663,"_672":81},{"_759":1153,"_722":1136,"_41":-5,"_297":761,"_762":-5,"_90":763},"{\"cmd\":\"nl -ba agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py | sed -n '300,520p'\"}",{"_670":1155,"_662":663,"_672":81},{"_673":-5,"_691":1156,"_41":1157,"_694":1158,"_90":696},"gAAAAABpaAjP3b95WzIRAzZ1qsqAkkkLQmNKVjnDbInXHAfFjcK6lR3EQWWeNALY8XLPgx1fd8HaGXGpYW-FCVb5BVDR05_RPDj61GcJMqE_POvTH2bk3eqSojlRyNBcY22Oaoov-wLcnew-hMIx3pjrWyHUEjvRjgdAzOTBiCFRAhfxU03d2pCU9UNc2wLhvv5z5Y2tjLUpKCrHfG8xJasVnJ9UQKDEm-RBqA7MqrmVgoX9lfB5KJVx-AcpE7_bKnGSWWm7yZfdvlKy7u7O5ajx3jrycByip3FB6lzmh6JWcd7g6p10v6ZDJlsUwyvPf6HTfIRvvUjWNv3RibE5aJUEawYZ7UjaUVIM4zXztqwg2wSpKm29bZgCS5-Zp5JzZZcUgFEoodgQjfE6l12k2J5OV5MWO5qBZ4ZzkLCPRVK76VobArAf2mFqu2D3pQTtKyEiRE0MRtkOjgOrlA9F6KQ8j7mmDxPJFCjeFUj-dZrgCENkvKNE19fvJyx7-aNRv-3E5si4xZR2lgguFrcNfPSfIFvIufxOR2O1NEWIy45oIs-__HLeWzSq6LTmZSrtsrYcQvFKWtW15pdjhOjdTAOBduEkO0IG-aNc1gq1omM2nGQHHW1ChaQ3GV5W25qmCxfVMX2Z3kI-jfw4d8yhqb4gtfbkN0zj3tBsKEeCLLIsKvf3xwhXrENen1y23ZvoYnZuCG7t8kEHb9Y1CKkLLZwIt00y9GNynKmyTWgeYwj9TV9NU_zG7feADvDAo6vpNq_3bAC5alxnzaang2mbXHV09XWHYp8bwO37MYjZca_dtZUj7pE_RPWqHEUv7wAiqKBdk84cpC-BOTn_J5yQI7tbvn4Z6uDU3oa1GQYItzAuW3nSAPLGsfREudKLY4-qv7rTCiOJWXfKNM_C3wCkoZ0BhHHFuvu8i8_cHkbox-NFHtV0sRECT5DgiRFpTcAW-XtVFnopldps8pO3WPdVU-1qswxSC3SgyNc2Ti9xNhT4uKzLXbfwQjlzrTor0YXe7dVUJAUMEPWAGWBVQdk9HVewPI0f7VKPBwMFwV9-QGlWPPsZSFdcCsh6IrqFEPtj-CDbb0F987Ket6SKRc8M877SctF4GmxrH77LU1EBVuGCckORazS3vl9A6J1S9Te3vIyRe2F1oX9_IVKz1a_QxJHi7u4wLYJiGSUB7XMRvQTP4np15T3KROvrvB1vG17C9QftcAJfFIWB7hxeMbowm6x2TRn9r-WOKHPEcFCctEfby_Bhx3dp184PA5p6jcuhQfWUAggmlOOfxySauTVL0JefHcSa41UUD8Mu1sTzLU-Cog7vwtfKa24POAw3_QFc_9OB8t22sG4Jf6t7tdFk4kTJw6Irp3L-XE2ejAj7lr4Q2mIns8QUo9DaDiL7qnEHL8Iy7D3dx_3lemDYQePD0mqeYeflaBHMSaf1IQAAf1tjZaeNvLfcnOZXVSK_n_fPVuEbnJxcpPyqvWISWjs1wiU59HoaiD3aL2aW0ndVBhL-DUanqhyy9cve-us86z-8zwg9-ftu8mJYd1vI53ydSlEecikYJVVrH3BrlRfB8lHwQEwwxf45OEEpMS9zLrjUHxKfbkOnPsk0","legacy_responseItem_reasoning_40",[1159],{"_45":1160,"_90":700},"**Identifying debug trace references**\n\nI'm focusing on adding accurate citations for debug trace capture, especially in evals_pydantic_mcp where new trace functions live, and considering whether to also reference run_multi_evals and run_single_evaluation for context. Next, I'll find relevant line numbers around trace context for precise documentation.",{"_670":1162,"_662":663,"_672":81},{"_673":1163,"_41":1164,"_694":1165,"_90":696},[1160],"legacy_reasoning_39",[],{"_670":1167,"_662":663,"_672":81},{"_673":1168,"_41":1164,"_694":1169,"_90":696},[],[],{"_670":1171,"_662":663,"_672":81},{"_722":1172,"_724":1173,"_90":726},"call_NUom19AGiEaN3daxxeFVtuYy",{"_408":1174,"_728":-5},"Chunk ID: 585a04\nWall time: 0.0515 seconds\nProcess exited with code 0\nOriginal token count: 861\nOutput:\n 300\t case.output\r\n 301\t and hasattr(case.output, \"metrics\")\r\n 302\t and isinstance(case.output.metrics, dict)\r\n 303\t ):\r\n 304\t requests_used = case.output.metrics.get(\"requests\")\r\n 305\t\r\n 306\t row.append(requests_used if requests_used is not None else \"na\")\r\n 307\t\r\n 308\t writer.writerow(row)\r\n 309\t\r\n 310\t return filepath\r\n 311\t\r\n 312\t\r\n 313\tclass MultiModelEvaluator:\r\n 314\t \"\"\"Main class for running evaluations across multiple models.\"\"\"\r\n 315\t\r\n 316\t def __init__(\r\n 317\t self,\r\n 318\t models: List[str],\r\n 319\t judge_model: str,\r\n 320\t output_dir: str = \"./results\",\r\n 321\t *,\r\n 322\t debug_traces: bool = False,\r\n 323\t trace_dir: Optional[str] = None,\r\n 324\t ):\r\n 325\t self.models = models\r\n 326\t self.judge_model = judge_model\r\n 327\t self.output_dir = output_dir\r\n 328\t self.console = Console()\r\n 329\t self.results: Dict[str, ModelEvaluationResults] = {}\r\n 330\t\r\n 331\t self._run_output_dir: Optional[str] = None\r\n 332\t self._retry_log_lock = asyncio.Lock()\r\n 333\t\r\n 334\t # Pre-create the per-invocation output directory to avoid races when running\r\n 335\t # evaluations in parallel.\r\n 336\t self._ensure_run_output_dir()\r\n 337\t\r\n 338\t self.debug_traces = debug_traces\r\n 339\t if trace_dir is not None:\r\n 340\t self.trace_dir = trace_dir\r\n 341\t elif debug_traces:\r\n 342\t self.trace_dir = os.path.join(self._ensure_run_output_dir(), \"debug_traces\")\r\n 343\t else:\r\n 344\t self.trace_dir = os.path.join(output_dir, \"debug_traces\")\r\n 345\t\r\n 346\t for model in models:\r\n 347\t self.results[model] = ModelEvaluationResults(model)\r\n 348\t\r\n 349\t def _ensure_run_output_dir(self) -> str:\r\n 350\t \"\"\"Return the per-invocation timestamped output directory, creating it if needed.\"\"\"\r\n 351\t if self._run_output_dir is not None:\r\n 352\t return self._run_output_dir\r\n 353\t\r\n 354\t timestamp = get_timestamp_prefix()\r\n 355\t base_dir = os.path.join(self.output_dir, timestamp)\r\n 356\t run_dir = base_dir\r\n 357\t suffix = 1\r\n 358\t while os.path.exists(run_dir):\r\n 359\t suffix += 1\r\n 360\t run_dir = f\"{base_dir}_{suffix}\"\r\n 361\t\r\n 362\t os.makedirs(run_dir, exist_ok=True)\r\n 363\t self._run_output_dir = run_dir\r\n 364\t\r\n 365\t # Create the retry log with a header so downstream parsing is easy.\r\n 366\t retry_log_path = os.path.join(run_dir, \"retries.log\")\r\n 367\t if not os.path.exists(retry_log_path):\r\n 368\t with open(retry_log_path, \"w\", newline=\"\", encoding=\"utf-8\") as file:\r\n 369\t writer = csv.writer(file)\r\n 370\t writer.writerow(\r\n 371\t [\r\n 372\t \"timestamp\",\r\n 373\t \"model\",\r\n 374\t \"planned_run\",\r\n 375\t \"attempt\",\r\n 376\t \"retry_reason\",\r\n 377\t \"delay_seconds\",\r\n 378\t ]\r\n 379\t )\r\n 380\t\r\n",{"_670":1176,"_662":663,"_672":81},{"_731":1177,"_733":1178,"_735":1179,"_737":738,"_739":740,"_741":31,"_41":1172,"_742":1180,"_744":745,"_667":111,"_90":746}," 300\t case.output\r\n 301\t and hasattr(case.output, \"metrics\")\r\n 302\t and isinstance(case.output.metrics, dict)\r\n 303\t ):\r\n 304\t requests_used = case.output.metrics.get(\"requests\")\r\n 305\t\r\n 306\t row.append(requests_used if requests_used is not None else \"na\")\r\n 307\t\r\n 308\t writer.writerow(row)\r\n 309\t\r\n 310\t return filepath\r\n 311\t\r\n 312\t\r\n 313\tclass MultiModelEvaluator:\r\n 314\t \"\"\"Main class for running evaluations across multiple models.\"\"\"\r\n 315\t\r\n 316\t def __init__(\r\n 317\t self,\r\n 318\t models: List[str],\r\n 319\t judge_model: str,\r\n 320\t output_dir: str = \"./results\",\r\n 321\t *,\r\n 322\t debug_traces: bool = False,\r\n 323\t trace_dir: Optional[str] = None,\r\n 324\t ):\r\n 325\t self.models = models\r\n 326\t self.judge_model = judge_model\r\n 327\t self.output_dir = output_dir\r\n 328\t self.console = Console()\r\n 329\t self.results: Dict[str, ModelEvaluationResults] = {}\r\n 330\t\r\n 331\t self._run_output_dir: Optional[str] = None\r\n 332\t self._retry_log_lock = asyncio.Lock()\r\n 333\t\r\n 334\t # Pre-create the per-invocation output directory to avoid races when running\r\n 335\t # evaluations in parallel.\r\n 336\t self._ensure_run_output_dir()\r\n 337\t\r\n 338\t self.debug_traces = debug_traces\r\n 339\t if trace_dir is not None:\r\n 340\t self.trace_dir = trace_dir\r\n 341\t elif debug_traces:\r\n 342\t self.trace_dir = os.path.join(self._ensure_run_output_dir(), \"debug_traces\")\r\n 343\t else:\r\n 344\t self.trace_dir = os.path.join(output_dir, \"debug_traces\")\r\n 345\t\r\n 346\t for model in models:\r\n 347\t self.results[model] = ModelEvaluationResults(model)\r\n 348\t\r\n 349\t def _ensure_run_output_dir(self) -> str:\r\n 350\t \"\"\"Return the per-invocation timestamped output directory, creating it if needed.\"\"\"\r\n 351\t if self._run_output_dir is not None:\r\n 352\t return self._run_output_dir\r\n 353\t\r\n 354\t timestamp = get_timestamp_prefix()\r\n 355\t base_dir = os.path.join(self.output_dir, timestamp)\r\n 356\t run_dir = base_dir\r\n 357\t suffix = 1\r\n 358\t while os.path.exists(run_dir):\r\n 359\t suffix += 1\r\n 360\t run_dir = f\"{base_dir}_{suffix}\"\r\n 361\t\r\n 362\t os.makedirs(run_dir, exist_ok=True)\r\n 363\t self._run_output_dir = run_dir\r\n 364\t\r\n 365\t # Create the retry log with a header so downstream parsing is easy.\r\n 366\t retry_log_path = os.path.join(run_dir, \"retries.log\")\r\n 367\t if not os.path.exists(retry_log_path):\r\n 368\t with open(retry_log_path, \"w\", newline=\"\", encoding=\"utf-8\") as file:\r\n 369\t writer = csv.writer(file)\r\n 370\t writer.writerow(\r\n 371\t [\r\n 372\t \"timestamp\",\r\n 373\t \"model\",\r\n 374\t \"planned_run\",\r\n 375\t \"attempt\",\r\n 376\t \"retry_reason\",\r\n 377\t \"delay_seconds\",\r\n 378\t ]\r\n 379\t )\r\n 380\t\r\n","/bin/bash -lc 'nl -ba agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py | sed -n '\"'\"'300,380p'\"'\"''",[1181],"22402",{"_733":1182,"_297":995,"_216":1183,"_90":751},"nl -ba agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py | sed -n '300,380p'","agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py",{"_670":1185,"_662":663,"_672":81},{"_731":-5,"_733":1178,"_735":1186,"_737":738,"_739":-5,"_741":-5,"_41":1172,"_742":1180,"_744":745,"_667":755,"_90":746},[1187],{"_733":1182,"_297":995,"_216":1183,"_90":751},{"_670":1189,"_662":663,"_672":81},{"_759":1190,"_722":1172,"_41":-5,"_297":761,"_762":-5,"_90":763},"{\"cmd\":\"nl -ba agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py | sed -n '300,380p'\"}",{"_670":1192,"_662":663,"_672":81},{"_673":-5,"_691":1193,"_41":1194,"_694":1195,"_90":696},"gAAAAABpaAjJ3PKKFiBsmrtMEA5LnUuOcis20MHOmXjDzDCIu_rpnQ4030cvqPlAkCrY0ZT4qUs0-291vxHTCIjDYgxKsLtCBMysP50QQtfIVT9GHrL24z-nBmnS-qOra9b3h0K86TxH6h2FRttb10OwnvAM2Cc6yisZ46-44iu0Zg1m0p4GO3fDZ6Pxg-KKYY_H1-ZXcb7InKbTY17R3GLF1XBWA8-U3fvl5H-i3FVime765-kG_PvkplxXoBc1SYKk_EWiyLF_28YxrXhburbWcr6__7pUbev1W5ihVLxcCrs0tRvphy7UNpn7CmlyzOvsKXbLUGLv8QtXas5BXLldQMNtsCYriwgakO7Pwjnn0z0q5lQj-pDyL8cdEmwEqw4MrlvFwTxPTDV43jaWKhiiuJuH35-zV1nxotgbG0Z3zDA7HU20XbiPoCUAaIPDs6GOxgCEe6C2jNU6guOonBqiq2NyV6YEhOXVShU42_GNZi1YOObz6TxwLmeLsliBF84ymUO96TGYMv9DqKYQId6rs16vdH2_qgk9-G6DsQ-CNDHVI4fTteEKb8FQGPR4caP-pSUrJvPqYFOEO-dO9rxW5rK9IIqNmsxo06IeGz0VQzgrIZbk6Ne4ITiEM289B2yrrp8rAALMLXDEz0ZCsAqDL8W3Oeaq2xdz1djzd8Q8K-tK5SNsVMxYg8cBsoCqZBrFtTYATfnZN2Nm0UjxtsmsPMFPRICykIar7cfwq-9bvNelTDrK4qA5xzq-5GLVEslT5Q-oS4aog9SdSYljgKw6MSsOz_sgVWatRSZ3UP_miryPsOOLWdtxtxU3TU4QZMF_Epw8Zv-r3puIdQHANIyEWDlW7XFqy212cNUTqRzK1DthD9lCtpT2mqZTW4Gs5M6skrqVTMkoU4Ujas3xEj4IOqIvrHrAvcccVc6IMKXI5e8vLIyI-CE=","legacy_responseItem_reasoning_38",[1196],{"_45":1197,"_90":700},"**Preparing context for debug trace inspection**",{"_670":1199,"_662":663,"_672":81},{"_673":1200,"_41":1201,"_694":1202,"_90":696},[1197],"legacy_reasoning_37",[],{"_670":1204,"_662":663,"_672":81},{"_673":1205,"_41":1201,"_694":1206,"_90":696},[],[],{"_670":1208,"_662":663,"_672":81},{"_722":1209,"_724":1210,"_90":726},"call_aB0YpyzeLn6JbXV0GCtGmHzG",{"_408":1211,"_728":-5},"Chunk ID: 6c082f\nWall time: 0.0513 seconds\nProcess exited with code 0\nOriginal token count: 164\nOutput:\n322: debug_traces: bool = False,\r\n338: self.debug_traces = debug_traces\r\n341: elif debug_traces:\r\n342: self.trace_dir = os.path.join(self._ensure_run_output_dir(), \"debug_traces\")\r\n344: self.trace_dir = os.path.join(output_dir, \"debug_traces\")\r\n521: if self.debug_traces:\r\n919: if self.debug_traces:\r\n986: \"--debug-traces\",\r\n989: help=\"Write per-case debug trace JSON files (prompt, full message history incl. tool calls/returns, usage)\",\r\n995: help=\"Directory to save debug traces (default: /debug_traces)\",\r\n1008: debug_traces=args.debug_traces,\r\n",{"_670":1213,"_662":663,"_672":81},{"_731":1214,"_733":1215,"_735":1216,"_737":738,"_739":740,"_741":31,"_41":1209,"_742":1217,"_744":745,"_667":111,"_90":746},"322: debug_traces: bool = False,\r\n338: self.debug_traces = debug_traces\r\n341: elif debug_traces:\r\n342: self.trace_dir = os.path.join(self._ensure_run_output_dir(), \"debug_traces\")\r\n344: self.trace_dir = os.path.join(output_dir, \"debug_traces\")\r\n521: if self.debug_traces:\r\n919: if self.debug_traces:\r\n986: \"--debug-traces\",\r\n989: help=\"Write per-case debug trace JSON files (prompt, full message history incl. tool calls/returns, usage)\",\r\n995: help=\"Directory to save debug traces (default: /debug_traces)\",\r\n1008: debug_traces=args.debug_traces,\r\n","/bin/bash -lc 'rg -n \"debug\" agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py'",[1218],"13615",{"_733":1219,"_216":995,"_955":1220,"_90":957},"rg -n debug agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py","debug",{"_670":1222,"_662":663,"_672":81},{"_731":-5,"_733":1215,"_735":1223,"_737":738,"_739":-5,"_741":-5,"_41":1209,"_742":1217,"_744":745,"_667":755,"_90":746},[1224],{"_733":1219,"_216":995,"_955":1220,"_90":957},{"_670":1226,"_662":663,"_672":81},{"_759":1227,"_722":1209,"_41":-5,"_297":761,"_762":-5,"_90":763},"{\"cmd\":\"rg -n \\\"debug\\\" agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py\"}",{"_670":1229,"_662":663,"_672":81},{"_673":-5,"_691":1230,"_41":1231,"_694":1232,"_90":696},"gAAAAABpaAjHsashh2hy9soElZWpkz8VwW8DLazyZ7lZaO2EWKDC6dfw3imYCwE9TEhP7zDWN62WbX1t-5qBWoldRrv4ErNcuTFGjO3Emr7Zv794nehxRITulkkByePWSzAzOnC-2MqzrFY0bqeiBWvvuoOdSJ98nUpU_BNVPiaTGwtKPLqSUE2SYzJpwhokT_iOqa_5Yn0Rp_30xt1_Upf9yGTVLefCXVQ4frqHDBVLZhQls6qAwxinQXd7lfdV5XS2bfTPYkoKH2dxUXtaubpyPsA_6M03U6kHNryZkfVnxQv2n4jyQRvWe88scnU9rMYTuwDTWxsQ64oAHph8KXOfBbMs5YUsQ9bZu2kFpdTPt1p3yAcCMUSIzX6AJbnhHJ-AwM-BezIJOVzYQtb2SC-eN-sTIQUeEGcGT3U1jTXhFBmkAVXs1zv5lTwoilEFz6-BMFgtWXohXBjn74I6mKE_3ejVFTO9dRy2SgrEf84GPAS40FYiZFphzU3-_83ogRlwPtgWdb965ygOTn4ElatKemHL-SWIq-5-Yfx7p1UjE6BPRuP_OkjEbY-zBnAdKaHoNvLgCqw2IkaTBYKFF0aCpxiOijWCuOe2w1_5-jO_Iv-0iZ0BMUkpyMqMiqWWzTDSpspVT0qgX28syRFNq8Klz_MQDaOYqo4GV4vS7pu0AzYyfYlEecaBZFB3U51J9QDzT-sRpgIp1sXuwyNJCw3_edS-RyVsRAsmF7vONUrE-29f3jSsHL7Kht6x9ORdS7vzesb12bFb_-uaqEzRhcvtPcorhdDlLAC_CcRm1THGy8GdW6eoW8kNiYvqb7LTdb_t8mA0RU_GuKVA4pFNw8J2jpBf_ON6eo0fo9ytpSTc0e4elY901iNmCmOw0C-VyQgl1qvtDv3lwDsPNZJFY-rWsW2W4Rt4ZkRWJ-hcS2XFiWqSHDtflc52SLDaFObLzEShyyaMTJ0ziS61d-_S3h6gdonItcEQO4Zde9iSN-tkcpJ-N696LSXKV2qjxRFnfbyKQhQnMUxHRCC6keNDPiffcW-r1aF28MRZntA8qYo00cRAN2o0EXBs9m4fH5_wCaDRUtNY0Rajt9sJTkrzpscADwUcZ_2NacwPgLBl0RkNH91qUn-QSM8R0Ez7w16bYT7uKpHPKBj-HwnOFs_WVOo2uYhQSj1BFA==","legacy_responseItem_reasoning_36",[1233],{"_45":1234,"_90":700},"**Searching for debug traces in run_multi_evals**",{"_670":1236,"_662":663,"_672":81},{"_673":1237,"_41":1238,"_694":1239,"_90":696},[1234],"legacy_reasoning_35",[],{"_670":1241,"_662":663,"_672":81},{"_673":1242,"_41":1238,"_694":1243,"_90":696},[],[],{"_670":1245,"_662":663,"_672":81},{"_722":1246,"_724":1247,"_90":726},"call_ffEzSAXNYlL9D2iLN3Obp1Vg",{"_408":1248,"_728":-5},"Chunk ID: e9179e\nWall time: 0.0513 seconds\nProcess exited with code 0\nOriginal token count: 739\nOutput:\n 500\t \"up_to\": \"inf\",\r\n 501\t \"price\": 0.1\r\n 502\t }\r\n 503\t ],\r\n 504\t \"output\": {\r\n 505\t \"default\": [\r\n 506\t {\r\n 507\t \"up_to\": \"inf\",\r\n 508\t \"price\": 0.4\r\n 509\t }\r\n 510\t ]\r\n 511\t }\r\n 512\t },\r\n 513\t \"openai:gpt-5\": {\r\n 514\t \"friendly_name\": \"GPT-5\",\r\n 515\t \"input\": [{\"up_to\": \"inf\", \"price\": 1.25}],\r\n 516\t \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 10.0}]}\r\n 517\t },\r\n 518\t \"openai:gpt-5-mini\": {\r\n 519\t \"friendly_name\": \"GPT-5 Mini\",\r\n 520\t \"input\": [{\"up_to\": \"inf\", \"price\": 0.25}],\r\n 521\t \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 2.0}]}\r\n 522\t },\r\n 523\t \"openai:gpt-5-nano\": {\r\n 524\t \"friendly_name\": \"GPT-5 Nano\",\r\n 525\t \"input\": [{\"up_to\": \"inf\", \"price\": 0.05}],\r\n 526\t \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 0.4}]}\r\n 527\t },\r\n 528\t \"openai:gpt-5-pro\": {\r\n 529\t \"friendly_name\": \"GPT-5 Pro\",\r\n 530\t \"input\": [{\"up_to\": \"inf\", \"price\": 15.0}],\r\n 531\t \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 120.0}]}\r\n 532\t },\r\n 533\t \"openai:gpt-5-chat-latest\": {\r\n 534\t \"friendly_name\": \"GPT-5 Chat Latest\",\r\n 535\t \"input\": [{\"up_to\": \"inf\", \"price\": 1.25}],\r\n 536\t \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 10.0}]}\r\n 537\t },\r\n 538\t \"openai:gpt-5-codex\": {\r\n 539\t \"friendly_name\": \"GPT-5 Codex\",\r\n 540\t \"input\": [{\"up_to\": \"inf\", \"price\": 1.25}],\r\n 541\t \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 10.0}]}\r\n 542\t },\r\n 543\t \"openai:gpt-5.1\": {\r\n 544\t \"friendly_name\": \"GPT-5.1\",\r\n 545\t \"input\": [{\"up_to\": \"inf\", \"price\": 1.25}],\r\n 546\t \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 10.0}]}\r\n 547\t },\r\n 548\t \"openai:gpt-5.1-codex\": {\r\n 549\t \"friendly_name\": \"GPT-5.1 Codex\",\r\n 550\t \"input\": [{\"up_to\": \"inf\", \"price\": 1.25}],\r\n 551\t \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 10.0}]}\r\n 552\t },\r\n 553\t \"openai:gpt-5.1-codex-max\": {\r\n 554\t \"friendly_name\": \"GPT-5.1 Codex Max\",\r\n 555\t \"input\": [{\"up_to\": \"inf\", \"price\": 1.25}],\r\n 556\t \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 10.0}]}\r\n 557\t },\r\n 558\t \"openai:gpt-5.1-codex-mini\": {\r\n 559\t \"friendly_name\": \"GPT-5.1 Codex Mini\",\r\n 560\t \"input\": [{\"up_to\": \"inf\", \"price\": 0.25}],\r\n 561\t \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 2.0}]}\r\n 562\t },\r\n 563\t \"openai:gpt-5-search-api\": {\r\n 564\t \"friendly_name\": \"GPT-5 Search API\",\r\n 565\t \"input\": [{\"up_to\": \"inf\", \"price\": 1.25}],\r\n 566\t \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 10.0}]}\r\n 567\t },\r\n 568\t \"openai:gpt-5.2\": {\r\n 569\t \"friendly_name\": \"GPT-5.2\",\r\n 570\t \"input\": [{\"up_to\": \"inf\", \"price\": 1.75}],\r\n",{"_670":1250,"_662":663,"_672":81},{"_731":1251,"_733":1252,"_735":1253,"_737":738,"_739":740,"_741":31,"_41":1246,"_742":1254,"_744":745,"_667":111,"_90":746}," 500\t \"up_to\": \"inf\",\r\n 501\t \"price\": 0.1\r\n 502\t }\r\n 503\t ],\r\n 504\t \"output\": {\r\n 505\t \"default\": [\r\n 506\t {\r\n 507\t \"up_to\": \"inf\",\r\n 508\t \"price\": 0.4\r\n 509\t }\r\n 510\t ]\r\n 511\t }\r\n 512\t },\r\n 513\t \"openai:gpt-5\": {\r\n 514\t \"friendly_name\": \"GPT-5\",\r\n 515\t \"input\": [{\"up_to\": \"inf\", \"price\": 1.25}],\r\n 516\t \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 10.0}]}\r\n 517\t },\r\n 518\t \"openai:gpt-5-mini\": {\r\n 519\t \"friendly_name\": \"GPT-5 Mini\",\r\n 520\t \"input\": [{\"up_to\": \"inf\", \"price\": 0.25}],\r\n 521\t \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 2.0}]}\r\n 522\t },\r\n 523\t \"openai:gpt-5-nano\": {\r\n 524\t \"friendly_name\": \"GPT-5 Nano\",\r\n 525\t \"input\": [{\"up_to\": \"inf\", \"price\": 0.05}],\r\n 526\t \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 0.4}]}\r\n 527\t },\r\n 528\t \"openai:gpt-5-pro\": {\r\n 529\t \"friendly_name\": \"GPT-5 Pro\",\r\n 530\t \"input\": [{\"up_to\": \"inf\", \"price\": 15.0}],\r\n 531\t \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 120.0}]}\r\n 532\t },\r\n 533\t \"openai:gpt-5-chat-latest\": {\r\n 534\t \"friendly_name\": \"GPT-5 Chat Latest\",\r\n 535\t \"input\": [{\"up_to\": \"inf\", \"price\": 1.25}],\r\n 536\t \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 10.0}]}\r\n 537\t },\r\n 538\t \"openai:gpt-5-codex\": {\r\n 539\t \"friendly_name\": \"GPT-5 Codex\",\r\n 540\t \"input\": [{\"up_to\": \"inf\", \"price\": 1.25}],\r\n 541\t \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 10.0}]}\r\n 542\t },\r\n 543\t \"openai:gpt-5.1\": {\r\n 544\t \"friendly_name\": \"GPT-5.1\",\r\n 545\t \"input\": [{\"up_to\": \"inf\", \"price\": 1.25}],\r\n 546\t \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 10.0}]}\r\n 547\t },\r\n 548\t \"openai:gpt-5.1-codex\": {\r\n 549\t \"friendly_name\": \"GPT-5.1 Codex\",\r\n 550\t \"input\": [{\"up_to\": \"inf\", \"price\": 1.25}],\r\n 551\t \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 10.0}]}\r\n 552\t },\r\n 553\t \"openai:gpt-5.1-codex-max\": {\r\n 554\t \"friendly_name\": \"GPT-5.1 Codex Max\",\r\n 555\t \"input\": [{\"up_to\": \"inf\", \"price\": 1.25}],\r\n 556\t \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 10.0}]}\r\n 557\t },\r\n 558\t \"openai:gpt-5.1-codex-mini\": {\r\n 559\t \"friendly_name\": \"GPT-5.1 Codex Mini\",\r\n 560\t \"input\": [{\"up_to\": \"inf\", \"price\": 0.25}],\r\n 561\t \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 2.0}]}\r\n 562\t },\r\n 563\t \"openai:gpt-5-search-api\": {\r\n 564\t \"friendly_name\": \"GPT-5 Search API\",\r\n 565\t \"input\": [{\"up_to\": \"inf\", \"price\": 1.25}],\r\n 566\t \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 10.0}]}\r\n 567\t },\r\n 568\t \"openai:gpt-5.2\": {\r\n 569\t \"friendly_name\": \"GPT-5.2\",\r\n 570\t \"input\": [{\"up_to\": \"inf\", \"price\": 1.75}],\r\n","/bin/bash -lc 'nl -ba agents_mcp_usage/evaluations/mermaid_evals/costs.json | sed -n '\"'\"'500,570p'\"'\"''",[1255],"94733",{"_733":1256,"_297":1257,"_216":1258,"_90":751},"nl -ba agents_mcp_usage/evaluations/mermaid_evals/costs.json | sed -n '500,570p'","costs.json","agents_mcp_usage/evaluations/mermaid_evals/costs.json",{"_670":1260,"_662":663,"_672":81},{"_731":-5,"_733":1252,"_735":1261,"_737":738,"_739":-5,"_741":-5,"_41":1246,"_742":1254,"_744":745,"_667":755,"_90":746},[1262],{"_733":1256,"_297":1257,"_216":1258,"_90":751},{"_670":1264,"_662":663,"_672":81},{"_759":1265,"_722":1246,"_41":-5,"_297":761,"_762":-5,"_90":763},"{\"cmd\":\"nl -ba agents_mcp_usage/evaluations/mermaid_evals/costs.json | sed -n '500,570p'\"}",{"_670":1267,"_662":663,"_672":81},{"_673":-5,"_691":1268,"_41":1269,"_694":1270,"_90":696},"gAAAAABpaAjE_QGoWIRGAUrFJkHYqMvhJ9nCOuea7R55AK-4218IMdZWIZZ5ZwNh6w8StSnj6RWE1ozoxba92nUGOACCylgB-H4zNlajy9owHjvLJZflyp_AHIxe_nuyEBlCNF-V8ZeejHhUnvUSWA5tcJ82QR3xWRKd3gszbqoI026av9VaRrnMnnW5C96RSK2gSXhmcaeLfeS-RvvBgi0sEVS8dTCpnUYcxw_61qyfNCt6oaal2WnG1DLoZpV0NEp3bJ1TXRjkp2g5Oj5NELBf4zF5gDPW93N8akSepKslhSyPceUjdbwpbi8kHjVD5rNhx7S5wRxUkiguXLuj5WS9wCRhewaw-173n_Thgw08kZK80j0LV3wRf-HlLrqFPiLT4ozzSvZdp7DQlLhUs9K2URsvlW9nXNF0feoUi1SyKJJlgXEpr__IwDolLSCQZxVaooMTl2BlbXI9VKF3x7pBHD6f6lvRX4qws31X7jAzbrN1_htiDsFlr0YRmbRhQdLm06P9-YnF4kAnSWdHVrNRY3jr4j2bLsWMFFbpSyaO99BMvX87I4TtxjKXYBUCIV4zEmywhOmK4AFveuyxmSpfRaPwHse8T4aTUbWH2swGawU64AWc44M9gb5ZHaMLXUyOPHeTHFmaEa1aJL4D-u4LlHk868KmjrssW91axk_52hQkL84gawgnIFb5z0qdAidJcZD58S8t4BSIKlPoDkLOFi-eV43dNUAk4itALRnL6fxU3kIo0EFoo_wxmkfkf033GBHjpwOo6ZZvUi8v3pwHigrEHbR1-wTFLMuZQtoy0KQKf05tlCUUo9w_Je9C2e52bwwo4D_bqfAPD7xOTTmMsiTObgOIN4YtSubKKU0O0o8VX0CT02Y=","legacy_responseItem_reasoning_34",[],{"_670":1272,"_662":663,"_672":81},{"_722":1273,"_724":1274,"_90":726},"call_L8VvLT1rjr3m2UiIX45l1Emz",{"_408":1275,"_728":-5},"Chunk ID: 41f69e\nWall time: 0.0512 seconds\nProcess exited with code 0\nOriginal token count: 85\nOutput:\n513: \"openai:gpt-5\": {\r\n518: \"openai:gpt-5-mini\": {\r\n523: \"openai:gpt-5-nano\": {\r\n528: \"openai:gpt-5-pro\": {\r\n533: \"openai:gpt-5-chat-latest\": {\r\n538: \"openai:gpt-5-codex\": {\r\n543: \"openai:gpt-5.1\": {\r\n548: \"openai:gpt-5.1-codex\": {\r\n553: \"openai:gpt-5.1-codex-max\": {\r\n558: \"openai:gpt-5.1-codex-mini\": {\r\n",{"_670":1277,"_662":663,"_672":81},{"_731":1278,"_733":1279,"_735":1280,"_737":738,"_739":740,"_741":31,"_41":1273,"_742":1281,"_744":745,"_667":111,"_90":746},"513: \"openai:gpt-5\": {\r\n518: \"openai:gpt-5-mini\": {\r\n523: \"openai:gpt-5-nano\": {\r\n528: \"openai:gpt-5-pro\": {\r\n533: \"openai:gpt-5-chat-latest\": {\r\n538: \"openai:gpt-5-codex\": {\r\n543: \"openai:gpt-5.1\": {\r\n548: \"openai:gpt-5.1-codex\": {\r\n553: \"openai:gpt-5.1-codex-max\": {\r\n558: \"openai:gpt-5.1-codex-mini\": {\r\n","/bin/bash -lc 'rg -n \"gpt-5\" agents_mcp_usage/evaluations/mermaid_evals/costs.json | head'",[1282],"5805",{"_733":1283,"_216":1257,"_955":1284,"_90":957},"rg -n gpt-5 agents_mcp_usage/evaluations/mermaid_evals/costs.json","gpt-5",{"_670":1286,"_662":663,"_672":81},{"_731":-5,"_733":1279,"_735":1287,"_737":738,"_739":-5,"_741":-5,"_41":1273,"_742":1281,"_744":745,"_667":755,"_90":746},[1288],{"_733":1283,"_216":1257,"_955":1284,"_90":957},{"_670":1290,"_662":663,"_672":81},{"_759":1291,"_722":1273,"_41":-5,"_297":761,"_762":-5,"_90":763},"{\"cmd\":\"rg -n \\\"gpt-5\\\" agents_mcp_usage/evaluations/mermaid_evals/costs.json | head\"}",{"_670":1293,"_662":663,"_672":81},{"_673":-5,"_691":1294,"_41":1295,"_694":1296,"_90":696},"gAAAAABpaAjBahFUam4hoU3NXm5lP2Gx5IND2UQrTk5RUIgcgGEdVMGcHuAKpm2mPYHQKHSWQKqFMrNp_C9qL0EOsYxCEtAAMilr6w9iK906C-GeSMWcxlnRsymf7qc_F1ySaIiNTXbCEK_KxdXPOjfWEAjBmlsLTw9jLiUnVhR0wasEJVLnlF8TBSBpSuCd6y1iS8JbYePEcH-8LbAhjmZiiP_BQHv6TAk3TS_MsUBtbd1rISx8tIMJfDWU1J6tC8bxL7wEYNjzh_bU3u3KNfkRB-mxf8sDbvCJZ8evUtjKsKkDcQZR3s1AliOPWhO0b0xyTatp-yZv1Kr_L20MNrbioTQoJn_qFagQT5orsFI2eGzOtm5aTjV4N9d0aW9Aiw5pHng7wH1jqVSOdyrnmvhnkx7haViwfNXVVH_xHeETbJS4Z-c1oe0sHPKdZhTiF74IMDR4nPMLZznDxwgGeWblPz1HwRbTE20iatPxQqbJTMv17JQTMUhb8flHWd7X8GcQNm-hu20p-9I5hO9eD55w1Ce_WdXxIozN2FHyeFd1vo8xlikGCUmzHChruj6F04DPjKnoRHOhk9Z-FTsM6eHbrbI6JMeIA0G1UMwtXk5jnPT2zBKqEAQuS-ZSeHplT1E_PG6bIF9w9-fVVsdOk-trtl3DHem5JUETdzK6dWAAyi65EMUH0hdtYzS1EOMxft0fJdcoEUFnFaLZgS7Cerl6Rt2M10xMKWKz9IC4dTqPiO3E83oP3W5JKBxKCHPSjpotX8iON3vhsq3aY37GPp_Kf-zEiVbZWC7sUYqlVlGmC6MY1t24xthkvGTBd1o52o7H65gksHzQM112EccCkJYHZUGPDpZOofPPLhwpNSl6vsjeBF9Yzs6zHx13vscXGGwy-vNz4CRV","legacy_responseItem_reasoning_33",[1297],{"_45":1298,"_90":700},"**Identifying line numbers for GPT-5 usage**",{"_670":1300,"_662":663,"_672":81},{"_673":1301,"_41":1302,"_694":1303,"_90":696},[1298],"legacy_reasoning_32",[],{"_670":1305,"_662":663,"_672":81},{"_673":1306,"_41":1302,"_694":1307,"_90":696},[],[],{"_670":1309,"_662":663,"_672":81},{"_722":1310,"_724":1311,"_90":726},"call_n67XcREQ8tTLhzAR89Vt9ZTW",{"_408":1312,"_728":-5},"Chunk ID: d1073d\nWall time: 0.0504 seconds\nProcess exited with code 0\nOriginal token count: 649\nOutput:\n 1\t{\r\n 2\t \"_comment\": \"Prices are per 1 million tokens - Updated July 19, 2025\",\r\n 3\t \"_sources\": {\r\n 4\t \"gemini\": \"https://ai.google.dev/gemini-api/docs/pricing\",\r\n 5\t \"openai\": \"https://openai.com/api/pricing/\",\r\n 6\t \"bedrock\": \"https://aws.amazon.com/bedrock/pricing/\",\r\n 7\t \"deepseek\": \"https://platform.deepseek.com/api-docs/pricing\",\r\n 8\t \"openrouter\": \"https://openrouter.ai/models\",\r\n 9\t \"github\": \"https://github.com/marketplace/models\",\r\n 10\t \"grok\": \"https://console.x.ai/pricing\",\r\n 11\t \"perplexity\": \"https://www.perplexity.ai/api/pricing\",\r\n 12\t \"fireworks\": \"https://fireworks.ai/pricing\",\r\n 13\t \"together\": \"https://www.together.ai/pricing\",\r\n 14\t \"heroku\": \"https://www.heroku.com/ai\"\r\n 15\t },\r\n 16\t \"model_costs\": {\r\n 17\t \"gemini-3-pro-preview\": {\r\n 18\t \"friendly_name\": \"Gemini 3.0 Pro Preview (Nov)\",\r\n 19\t \"input\": [\r\n 20\t {\r\n 21\t \"up_to\": 200000,\r\n 22\t \"price\": 2.0\r\n 23\t },\r\n 24\t {\r\n 25\t \"up_to\": \"inf\",\r\n 26\t \"price\": 4.0\r\n 27\t }\r\n 28\t ],\r\n 29\t \"output\": {\r\n 30\t \"default\": [\r\n 31\t {\r\n 32\t \"up_to\": 200000,\r\n 33\t \"price\": 12.0\r\n 34\t },\r\n 35\t {\r\n 36\t \"up_to\": \"inf\",\r\n 37\t \"price\": 18.0\r\n 38\t }\r\n 39\t ]\r\n 40\t }\r\n 41\t },\r\n 42\t \"gemini-2.5-pro-preview-03-25\": {\r\n 43\t \"friendly_name\": \"Gemini 2.5 Pro Preview (Mar)\",\r\n 44\t \"input\": [\r\n 45\t {\r\n 46\t \"up_to\": 200000,\r\n 47\t \"price\": 1.25\r\n 48\t },\r\n 49\t {\r\n 50\t \"up_to\": \"inf\",\r\n 51\t \"price\": 2.5\r\n 52\t }\r\n 53\t ],\r\n 54\t \"output\": {\r\n 55\t \"default\": [\r\n 56\t {\r\n 57\t \"up_to\": 200000,\r\n 58\t \"price\": 10.0\r\n 59\t },\r\n 60\t {\r\n 61\t \"up_to\": \"inf\",\r\n 62\t \"price\": 15.0\r\n 63\t }\r\n 64\t ]\r\n 65\t }\r\n 66\t },\r\n 67\t \"gemini-2.5-pro-preview-05-06\": {\r\n 68\t \"friendly_name\": \"Gemini 2.5 Pro Preview (May)\",\r\n 69\t \"input\": [\r\n 70\t {\r\n 71\t \"up_to\": 200000,\r\n 72\t \"price\": 1.25\r\n 73\t },\r\n 74\t {\r\n 75\t \"up_to\": \"inf\",\r\n 76\t \"price\": 2.5\r\n 77\t }\r\n 78\t ],\r\n 79\t \"output\": {\r\n 80\t \"default\": [\r\n",{"_670":1314,"_662":663,"_672":81},{"_731":1315,"_733":1316,"_735":1317,"_737":738,"_739":801,"_741":31,"_41":1310,"_742":1318,"_744":745,"_667":111,"_90":746}," 1\t{\r\n 2\t \"_comment\": \"Prices are per 1 million tokens - Updated July 19, 2025\",\r\n 3\t \"_sources\": {\r\n 4\t \"gemini\": \"https://ai.google.dev/gemini-api/docs/pricing\",\r\n 5\t \"openai\": \"https://openai.com/api/pricing/\",\r\n 6\t \"bedrock\": \"https://aws.amazon.com/bedrock/pricing/\",\r\n 7\t \"deepseek\": \"https://platform.deepseek.com/api-docs/pricing\",\r\n 8\t \"openrouter\": \"https://openrouter.ai/models\",\r\n 9\t \"github\": \"https://github.com/marketplace/models\",\r\n 10\t \"grok\": \"https://console.x.ai/pricing\",\r\n 11\t \"perplexity\": \"https://www.perplexity.ai/api/pricing\",\r\n 12\t \"fireworks\": \"https://fireworks.ai/pricing\",\r\n 13\t \"together\": \"https://www.together.ai/pricing\",\r\n 14\t \"heroku\": \"https://www.heroku.com/ai\"\r\n 15\t },\r\n 16\t \"model_costs\": {\r\n 17\t \"gemini-3-pro-preview\": {\r\n 18\t \"friendly_name\": \"Gemini 3.0 Pro Preview (Nov)\",\r\n 19\t \"input\": [\r\n 20\t {\r\n 21\t \"up_to\": 200000,\r\n 22\t \"price\": 2.0\r\n 23\t },\r\n 24\t {\r\n 25\t \"up_to\": \"inf\",\r\n 26\t \"price\": 4.0\r\n 27\t }\r\n 28\t ],\r\n 29\t \"output\": {\r\n 30\t \"default\": [\r\n 31\t {\r\n 32\t \"up_to\": 200000,\r\n 33\t \"price\": 12.0\r\n 34\t },\r\n 35\t {\r\n 36\t \"up_to\": \"inf\",\r\n 37\t \"price\": 18.0\r\n 38\t }\r\n 39\t ]\r\n 40\t }\r\n 41\t },\r\n 42\t \"gemini-2.5-pro-preview-03-25\": {\r\n 43\t \"friendly_name\": \"Gemini 2.5 Pro Preview (Mar)\",\r\n 44\t \"input\": [\r\n 45\t {\r\n 46\t \"up_to\": 200000,\r\n 47\t \"price\": 1.25\r\n 48\t },\r\n 49\t {\r\n 50\t \"up_to\": \"inf\",\r\n 51\t \"price\": 2.5\r\n 52\t }\r\n 53\t ],\r\n 54\t \"output\": {\r\n 55\t \"default\": [\r\n 56\t {\r\n 57\t \"up_to\": 200000,\r\n 58\t \"price\": 10.0\r\n 59\t },\r\n 60\t {\r\n 61\t \"up_to\": \"inf\",\r\n 62\t \"price\": 15.0\r\n 63\t }\r\n 64\t ]\r\n 65\t }\r\n 66\t },\r\n 67\t \"gemini-2.5-pro-preview-05-06\": {\r\n 68\t \"friendly_name\": \"Gemini 2.5 Pro Preview (May)\",\r\n 69\t \"input\": [\r\n 70\t {\r\n 71\t \"up_to\": 200000,\r\n 72\t \"price\": 1.25\r\n 73\t },\r\n 74\t {\r\n 75\t \"up_to\": \"inf\",\r\n 76\t \"price\": 2.5\r\n 77\t }\r\n 78\t ],\r\n 79\t \"output\": {\r\n 80\t \"default\": [\r\n","/bin/bash -lc 'nl -ba agents_mcp_usage/evaluations/mermaid_evals/costs.json | sed -n '\"'\"'1,80p'\"'\"''",[1319],"3003",{"_733":1320,"_297":1257,"_216":1258,"_90":751},"nl -ba agents_mcp_usage/evaluations/mermaid_evals/costs.json | sed -n '1,80p'",{"_670":1322,"_662":663,"_672":81},{"_731":-5,"_733":1316,"_735":1323,"_737":738,"_739":-5,"_741":-5,"_41":1310,"_742":1318,"_744":745,"_667":755,"_90":746},[1324],{"_733":1320,"_297":1257,"_216":1258,"_90":751},{"_670":1326,"_662":663,"_672":81},{"_759":1327,"_722":1310,"_41":-5,"_297":761,"_762":-5,"_90":763},"{\"cmd\":\"nl -ba agents_mcp_usage/evaluations/mermaid_evals/costs.json | sed -n '1,80p'\"}",{"_670":1329,"_662":663,"_672":81},{"_673":-5,"_691":1330,"_41":1331,"_694":1332,"_90":696},"gAAAAABpaAi_uCTMyZNn6A9A-D9lTmWuadZ63tG65JNUywdgXZgt71LGoMIhyu2PMXTgKYlpJpnf6y4S9qh8yGZBra4hr60uuLAwPMnGuWBxQEUC8nxhte5Pzn-vd4cnvtKxtLHkzVvp3T2nFF_ZHExOIAdUrrF9GrkL2hEDaPpxyOsKFta9I_OW9swLJ1txUcpS8WJNLN8I5tHyE_d8gfcDrXf6Vzobuu3FTq2oTojNA1xBQDKUhP3UgxZXmOL4ioV7YgGs5xlSV7YsAhVtWn-8Tf8OZwrl2ZXB4C4YFN6lZil2tCH6qbZemOST6Z6syj_4Yv6P1QtbkxAnoVUu8acpv83qG9vgyMXgYd8P2me0YI8Vl1i330OqRCjicaFqTT6EwongjUFM3m8cr5EPBjGy7Ls9Wr9osLy5jiTtPZPr1WuKdNmYmw72oJdx3OtA-jXqYIRCO0PNNuvGPoNuQWn0NaUveuCoDNox4PNM44uyxj6RZMLaavPzeH-ANHvZ--xVUPgigLISJRrx0b59zFbokUuW7bUu66u3JPuF6wiY2aWhifUYttlC7nV1XCmIiihE_Db91j65sESXUb3PmW3Uya948fceRNQr3iXkJNNAZCyTgVRNhPi5KebJDOvl8NJCXnba-OnrArlvm4BmdkUQ0GPs-36709fW5rx1MdVtmIwqBaCh8rUXxXV7r-N7OFnvwSMnd1DeWm7fTXIiMYAID6VR_5NhgLeh7bZEdGu90sv4lbx1RoYj5bXGSoaWf7PcQJxHj6SF397OQSak9i_7Gv_0ITMyx-Xlo2drleczGzjNTuq1dL6mujycLgDLDG6I54QWlNU--rwHlvKb1eYGaCcA_0bIJJDASUMSseNBW78lMLrSHW5xI8S0BkgwJ6A8YlIh7i3ojB7D7QtTbLJwGa_fOC7acwqB1houxfGnhtFzN9ivvlVFT-ZtRXWrDF_kBwjaWj4K793sT8pKv0LXckL3RIGMYeTKYxCDnaSt4_UT61fusmeW8D5-E_k-LGCC9dHThOUrRKiwaCXe8qMUMWK91VbAIb5fgK4f3WTrO6_htd1HDoLK988KR3xJS-p-NYGlBQPAGPPeHV1_VCs8zJykKwZ3rCqdRtMFWHfqrc0wwu2Xyxtp22GjCBNo7lfatUX_a6nD6okVYhx1_-_QEV3WQyYVYRHQh0t0Ubb0DQ2dB67lFkYmdNfeFV0Kl95ZxmfiiARR","legacy_responseItem_reasoning_31",[1333],{"_45":1334,"_90":700},"**Planning cost citation extraction**\n\nI'm preparing to extract and cite updated cost entries from costs.json by locating line numbers around new models like gemini-3 and openai gpt-5 using line-number tools for precise summary references.",{"_670":1336,"_662":663,"_672":81},{"_673":1337,"_41":1338,"_694":1339,"_90":696},[1334],"legacy_reasoning_30",[],{"_670":1341,"_662":663,"_672":81},{"_673":1342,"_41":1338,"_694":1343,"_90":696},[],[],{"_670":1345,"_662":663,"_672":81},{"_722":1346,"_724":1347,"_90":726},"call_v5y3li1C17gcgfJe92h4PK7P",{"_408":1348,"_728":-5},"Chunk ID: 22a6af\nWall time: 0.0513 seconds\nProcess exited with code 0\nOriginal token count: 35\nOutput:\n 1\tGEMINI_API_KEY=\r\n 2\tOPENAI_API_KEY=\r\n 3\tOPENROUTER_API_KEY=\r\n 4\tLOGFIRE_TOKEN=\r\n 5\tAWS_REGION=\r\n 6\tAWS_PROFILE=\r\n",{"_670":1350,"_662":663,"_672":81},{"_731":1351,"_733":1352,"_735":1353,"_737":738,"_739":740,"_741":31,"_41":1346,"_742":1354,"_744":745,"_667":111,"_90":746}," 1\tGEMINI_API_KEY=\r\n 2\tOPENAI_API_KEY=\r\n 3\tOPENROUTER_API_KEY=\r\n 4\tLOGFIRE_TOKEN=\r\n 5\tAWS_REGION=\r\n 6\tAWS_PROFILE=\r\n","/bin/bash -lc 'nl -ba .env.example'",[1355],"84477",{"_733":1356,"_297":1357,"_216":1357,"_90":751},"nl -ba .env.example",".env.example",{"_670":1359,"_662":663,"_672":81},{"_731":-5,"_733":1352,"_735":1360,"_737":738,"_739":-5,"_741":-5,"_41":1346,"_742":1354,"_744":745,"_667":755,"_90":746},[1361],{"_733":1356,"_297":1357,"_216":1357,"_90":751},{"_670":1363,"_662":663,"_672":81},{"_759":1364,"_722":1346,"_41":-5,"_297":761,"_762":-5,"_90":763},"{\"cmd\":\"nl -ba .env.example\"}",{"_670":1366,"_662":663,"_672":81},{"_673":-5,"_691":1367,"_41":1368,"_694":1369,"_90":696},"gAAAAABpaAi7Ey7xBwzyH-XMYAhMmMP5jMN5AVaBvzZ9VCAS-DUHBDEpNCCtvM0TxyN-hRSXBzGi9Vk4NiWkWoj4q5mlrC0WPyYzWg5q45g8orqPId8dwQE-SxqjA1PwGuXjdSOg22_DuXfS_R5KtZNypTs5RanYhoSEK8Tw2IiW1dzKi0p3hd7DVRokxhhTHaVJYXoJNspF1CPNiCr4IX1_v6crcw4pxB6n1wBnXH5ldqUaVkOEt-S3D99yzZbuBw0nL1JWHAu-H_15wez6Kat7JcY51TzJ66Xjn-iHthQgp7JxyzxSxc_sf03s9etSnHWDespwotUzuRZRhOcrul89qFZjE2U0Ex4X6H-5TAA0hz3zONzAsjtDdbq2OwuBYPdvMqSo53sluvwb-W0Q-eNMLu6grUMhWBNcRNmprxEUs0pSdrTg3OszwxG3s_aFwU7DegUjkLvmlmV92mtLSdSkmF_q9wXJmOOvP2wiwcs-giBHClX4bJRuCb7MqwlIr9myOfvjb4SwkSBvDotK37n4ZoV8trRpBfKuse-Du2VRz__Zd1COLBm9fGm3QFueRfj2wgnzELovn8xi3L5dvOhMo-WleWmn0acDBfRz7LmekYu1Y4ijDyku1xJffCvD_A0RSA9MpGH8TgjHaNJJ8dt8st5b6cfciA9D7EvR2wOe6r4tE41Z7wb6mVPWi65tcTZcFODChhXJr1-djulKovRkdPjitIR6j0SUOivMJsPLYIe1LL9t73ns-YdwHdmDMjKs7PaJDNumA09KBSw2Sb8ZYgr4OWMvg2Sbgon3jYDEk8lAiue2os_-5S_a3rq9w4KUBV7_uFWNFGZB-OIusq3gRPwqkCEt5ojNmb15HEELhUXiZxst8Lg=","legacy_responseItem_reasoning_29",[1370],{"_45":1371,"_90":700},"**Preparing .env.example review**\n\nI'm collecting line numbers from .env.example to reference specific environment variables for clearer context in upcoming tasks.",{"_670":1373,"_662":663,"_672":81},{"_673":1374,"_41":1375,"_694":1376,"_90":696},[1371],"legacy_reasoning_28",[],{"_670":1378,"_662":663,"_672":81},{"_673":1379,"_41":1375,"_694":1380,"_90":696},[],[],{"_670":1382,"_662":663,"_672":81},{"_722":1383,"_724":1384,"_90":726},"call_Ek0b2oEmidRnGJdMlc7juhip",{"_408":1385,"_728":-5},"Chunk ID: 206c06\nWall time: 0.0515 seconds\nProcess exited with code 0\nOriginal token count: 1057\nOutput:\n 360\tuv run agents_mcp_usage/evaluations/mermaid_evals/scripts/preprocess_merbench_data.py \\\r\n 361\t -i mermaid_eval_results/machine_b_results.csv \\\r\n 362\t -o results/machine_b.json\r\n 363\t```\r\n 364\t\r\n 365\t#### 3. Merge results:\r\n 366\t```bash\r\n 367\tuv run agents_mcp_usage/evaluations/mermaid_evals/scripts/merge_benchmark_results.py \\\r\n 368\t -i results/machine_a.json results/machine_b.json \\\r\n 369\t -o results/combined.json \\\r\n 370\t --report results/merge_summary.json\r\n 371\t```\r\n 372\t\r\n 373\t### Notes\r\n 374\t- The script automatically recalculates all statistics from the merged raw data\r\n 375\t- Cost calculations use the same `costs.json` configuration as the preprocessing script\r\n 376\t- Provider detection is based on model name patterns (gemini→Google, nova→Amazon, etc.)\r\n 377\t\r\n 378\t## Monitoring & Debugging\r\n 379\t\r\n 380\t### Logfire (remote, if configured)\r\n 381\t\r\n 382\tAll evaluation runs are traced with **Logfire** for comprehensive monitoring:\r\n 383\t- Tool call traces\r\n 384\t- Retry attempts and reasons\r\n 385\t- Execution durations\r\n 386\t- Categorised failure analysis\r\n 387\t\r\n 388\t### Local per-case debug traces (JSON)\r\n 389\t\r\n 390\tWhen diagnosing failure modes across models (e.g. tool-call loops, request/turn limits, redundant tool usage), you can enable a local debug mode that writes a detailed per-case trace JSON file for each model attempt.\r\n 391\t\r\n 392\tEnable traces:\r\n 393\t\r\n 394\t```bash\r\n 395\tuv run agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py \\\r\n 396\t --runs 1 \\\r\n 397\t --sequential \\\r\n 398\t --debug-traces\r\n 399\t```\r\n 400\t\r\n 401\tOptional: choose where traces are written:\r\n 402\t\r\n 403\t```bash\r\n 404\tuv run agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py \\\r\n 405\t --runs 1 \\\r\n 406\t --sequential \\\r\n 407\t --debug-traces \\\r\n 408\t --trace-dir ./mermaid_eval_results/debug_traces\r\n 409\t```\r\n 410\t\r\n 411\tBy default, the trace directory is:\r\n 412\t\r\n 413\t- `/debug_traces/` (where `` defaults to `./mermaid_eval_results`)\r\n 414\t\r\n 415\t#### Output layout\r\n 416\t\r\n 417\tTraces are stored under a per-model subdirectory:\r\n 418\t\r\n 419\t```text\r\n 420\t/\r\n 421\t /\r\n 422\t model=__run###__attempt###__case=.json\r\n 423\t```\r\n 424\t\r\n 425\t> `run###` is the planned run number (1-based), `attempt###` is the runner-level retry attempt (1-based), and `case_slug` is derived from the case name (or a hash of the input if absent).\r\n 426\t\r\n 427\t#### What gets captured\r\n 428\t\r\n 429\tEach trace JSON file includes (at minimum):\r\n 430\t\r\n 431\t- input: invalid diagram + hash + case name\r\n 432\t- prompt query sent to the agent\r\n 433\t- full message history (including tool call args and tool return payloads)\r\n 434\t- derived message summary:\r\n 435\t - request count, response count\r\n 436\t - tool call/return counts\r\n 437\t - tool name sequence (helps spot loops)\r\n 438\t- usage limits used for the run\r\n 439\t- usage/tokens (when available from the provider)\r\n 440\t- output text and extracted mermaid diagram (on success)\r\n 441\t- exception metadata and traceback (on failures)\r\n 442\t\r\n 443\t**Important:** traces contain full prompts and full tool return payloads. Treat them as sensitive and avoid sharing publicly.\r\n 444\t\r\n 445\t## Troubleshooting\r\n 446\t\r\n 447\t### Common Issues\r\n 448\t\r\n 449\t1. **API Key Errors**\r\n 450\t - Ensure environment variables are set correctly\r\n 451\t - Check API key permissions and quotas\r\n 452\t\r\n 453\t2. **MCP Server Connection Issues**\r\n 454\t - Verify MCP servers are properly installed\r\n 455\t - Check server process logs\r\n 456\t\r\n 457\t3. **Validation Failures**\r\n 458\t - Review the fixed diagram for syntax errors\r\n 459\t - Check Mermaid version compatibility\r\n 460\t\r\n 461\t4. **Performance Issues**\r\n 462\t - Reduce parallel execution for rate limits\r\n 463\t - Monitor token usage per model\r\n 464\t\r\n 465\t## Advanced Usage\r\n 466\t\r\n 467\t### Custom Model Configuration\r\n 468\t\r\n 469\tYou can customise model parameters when running evaluations:\r\n 470\t\r\n",{"_670":1387,"_662":663,"_672":81},{"_731":1388,"_733":1389,"_735":1390,"_737":738,"_739":740,"_741":31,"_41":1383,"_742":1391,"_744":745,"_667":111,"_90":746}," 360\tuv run agents_mcp_usage/evaluations/mermaid_evals/scripts/preprocess_merbench_data.py \\\r\n 361\t -i mermaid_eval_results/machine_b_results.csv \\\r\n 362\t -o results/machine_b.json\r\n 363\t```\r\n 364\t\r\n 365\t#### 3. Merge results:\r\n 366\t```bash\r\n 367\tuv run agents_mcp_usage/evaluations/mermaid_evals/scripts/merge_benchmark_results.py \\\r\n 368\t -i results/machine_a.json results/machine_b.json \\\r\n 369\t -o results/combined.json \\\r\n 370\t --report results/merge_summary.json\r\n 371\t```\r\n 372\t\r\n 373\t### Notes\r\n 374\t- The script automatically recalculates all statistics from the merged raw data\r\n 375\t- Cost calculations use the same `costs.json` configuration as the preprocessing script\r\n 376\t- Provider detection is based on model name patterns (gemini→Google, nova→Amazon, etc.)\r\n 377\t\r\n 378\t## Monitoring & Debugging\r\n 379\t\r\n 380\t### Logfire (remote, if configured)\r\n 381\t\r\n 382\tAll evaluation runs are traced with **Logfire** for comprehensive monitoring:\r\n 383\t- Tool call traces\r\n 384\t- Retry attempts and reasons\r\n 385\t- Execution durations\r\n 386\t- Categorised failure analysis\r\n 387\t\r\n 388\t### Local per-case debug traces (JSON)\r\n 389\t\r\n 390\tWhen diagnosing failure modes across models (e.g. tool-call loops, request/turn limits, redundant tool usage), you can enable a local debug mode that writes a detailed per-case trace JSON file for each model attempt.\r\n 391\t\r\n 392\tEnable traces:\r\n 393\t\r\n 394\t```bash\r\n 395\tuv run agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py \\\r\n 396\t --runs 1 \\\r\n 397\t --sequential \\\r\n 398\t --debug-traces\r\n 399\t```\r\n 400\t\r\n 401\tOptional: choose where traces are written:\r\n 402\t\r\n 403\t```bash\r\n 404\tuv run agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py \\\r\n 405\t --runs 1 \\\r\n 406\t --sequential \\\r\n 407\t --debug-traces \\\r\n 408\t --trace-dir ./mermaid_eval_results/debug_traces\r\n 409\t```\r\n 410\t\r\n 411\tBy default, the trace directory is:\r\n 412\t\r\n 413\t- `/debug_traces/` (where `` defaults to `./mermaid_eval_results`)\r\n 414\t\r\n 415\t#### Output layout\r\n 416\t\r\n 417\tTraces are stored under a per-model subdirectory:\r\n 418\t\r\n 419\t```text\r\n 420\t/\r\n 421\t /\r\n 422\t model=__run###__attempt###__case=.json\r\n 423\t```\r\n 424\t\r\n 425\t> `run###` is the planned run number (1-based), `attempt###` is the runner-level retry attempt (1-based), and `case_slug` is derived from the case name (or a hash of the input if absent).\r\n 426\t\r\n 427\t#### What gets captured\r\n 428\t\r\n 429\tEach trace JSON file includes (at minimum):\r\n 430\t\r\n 431\t- input: invalid diagram + hash + case name\r\n 432\t- prompt query sent to the agent\r\n 433\t- full message history (including tool call args and tool return payloads)\r\n 434\t- derived message summary:\r\n 435\t - request count, response count\r\n 436\t - tool call/return counts\r\n 437\t - tool name sequence (helps spot loops)\r\n 438\t- usage limits used for the run\r\n 439\t- usage/tokens (when available from the provider)\r\n 440\t- output text and extracted mermaid diagram (on success)\r\n 441\t- exception metadata and traceback (on failures)\r\n 442\t\r\n 443\t**Important:** traces contain full prompts and full tool return payloads. Treat them as sensitive and avoid sharing publicly.\r\n 444\t\r\n 445\t## Troubleshooting\r\n 446\t\r\n 447\t### Common Issues\r\n 448\t\r\n 449\t1. **API Key Errors**\r\n 450\t - Ensure environment variables are set correctly\r\n 451\t - Check API key permissions and quotas\r\n 452\t\r\n 453\t2. **MCP Server Connection Issues**\r\n 454\t - Verify MCP servers are properly installed\r\n 455\t - Check server process logs\r\n 456\t\r\n 457\t3. **Validation Failures**\r\n 458\t - Review the fixed diagram for syntax errors\r\n 459\t - Check Mermaid version compatibility\r\n 460\t\r\n 461\t4. **Performance Issues**\r\n 462\t - Reduce parallel execution for rate limits\r\n 463\t - Monitor token usage per model\r\n 464\t\r\n 465\t## Advanced Usage\r\n 466\t\r\n 467\t### Custom Model Configuration\r\n 468\t\r\n 469\tYou can customise model parameters when running evaluations:\r\n 470\t\r\n","/bin/bash -lc 'nl -ba agents_mcp_usage/evaluations/mermaid_evals/README.md | sed -n '\"'\"'360,470p'\"'\"''",[1392],"33599",{"_733":1393,"_297":749,"_216":1394,"_90":751},"nl -ba agents_mcp_usage/evaluations/mermaid_evals/README.md | sed -n '360,470p'","agents_mcp_usage/evaluations/mermaid_evals/README.md",{"_670":1396,"_662":663,"_672":81},{"_731":-5,"_733":1389,"_735":1397,"_737":738,"_739":-5,"_741":-5,"_41":1383,"_742":1391,"_744":745,"_667":755,"_90":746},[1398],{"_733":1393,"_297":749,"_216":1394,"_90":751},{"_670":1400,"_662":663,"_672":81},{"_759":1401,"_722":1383,"_41":-5,"_297":761,"_762":-5,"_90":763},"{\"cmd\":\"nl -ba agents_mcp_usage/evaluations/mermaid_evals/README.md | sed -n '360,470p'\"}",{"_670":1403,"_662":663,"_672":81},{"_673":-5,"_691":1404,"_41":1405,"_694":1406,"_90":696},"gAAAAABpaAi4ll-lWLjS7paVU98Pq8bFlQUYGYwkEOJQ-CGTNJReukIYBvxuRnKidfz6MWNKEV0VRJ-3ytPFHnA9elO_NHvR5oC_2azJ1I5LKJPyMjzNuZ2frwtHmATjuYmbfCVkTAnFtMZvVKx1kIKW9rr9jL6c1FdN03zwZ0bzQanxdjUU4jaOpFaUGTyGl39zpdmXv60A8lJ1hA3QJM-Xt7XNOZZsdthIxH9Izvf0h_KcqnHkw5jn2cuIZvYbeXI-azYIRnYQoetswXpA_FnapRzI1aSA1v2lGIzqj5s_0KqlYqwroURDDy8rbtywaVftN6Ie5o_P6DcgigrI7o7_lxCyd0zjw2NyxmnDzGarjGL0f0R5LyeYZTrpcXdCv-eKH93NUy8jUT4cR6r9lu4w1MWVEvKTiQPZNVEaktMn0TpnEtInlJRhF1riuwhzzMx9mH0E_3iNP1Y1h0aWNUXBXATkMizYKxc2_s0SQByLtqUBgglG1-hVXNpyMfpeK1h3vfn9JOPsA8WvXOUIAM6vqtbfMl98T_wQV4YyPESWI9bauJhGPwJ7mdVyJ7mowWignUWinGRAmK3pQkQBph2A2a_b86dW0Jzx8RPGExMLxzZSdZSq2ezwLHUROMKTCv1ODRyQZAFXWCpAK2G5GiJRAEmAJhYM1ALzjiCx_RR7hrPVMWt12GIIAIcWPhuPI6ouUCsXH9rkAhOCLwg4OjeQwPMbpm7jUg6ZvPUsFLInpFTpGXXPPnGdxDgMpS0XMx3DGW46TPa0pvPUzYVsWaiT7XCtn42HvssNpWAjDgZ_2fdmKDyL-ss6SM4pPi3xa-bmUnERBK4yHpnxSWPrTmOEC3tqOI8mtSFAyKfrz7EcGkPOB-J9piENumaV7Inps6kjfIYIEGIUNDEeyiI0UDBvyCzfJCoIzAvkp8oVNnSmbevcPvWdCrs=","legacy_responseItem_reasoning_27",[1407],{"_45":1408,"_90":700},"**Planning debug trace section**",{"_670":1410,"_662":663,"_672":81},{"_673":1411,"_41":1412,"_694":1413,"_90":696},[1408],"legacy_reasoning_26",[],{"_670":1415,"_662":663,"_672":81},{"_673":1416,"_41":1412,"_694":1417,"_90":696},[],[],{"_670":1419,"_662":663,"_672":81},{"_722":1420,"_724":1421,"_90":726},"call_hADdfezmdOzhXRvH391GEuFk",{"_408":1422,"_728":-5},"Chunk ID: 3e36eb\nWall time: 0.0504 seconds\nProcess exited with code 0\nOriginal token count: 1127\nOutput:\n 60\t\r\n 61\t### Configuration\r\n 62\tThe evaluation system uses Pydantic-AI and requires environment variables for API keys:\r\n 63\t```bash\r\n 64\t# Required for most models\r\n 65\texport GEMINI_API_KEY=\"your-key-here\"\r\n 66\t\r\n 67\t# Optional for specific providers/models\r\n 68\texport OPENAI_API_KEY=\"your-key-here\"\r\n 69\texport OPENROUTER_API_KEY=\"your-key-here\" # required for `openrouter:*` models\r\n 70\t```\r\n 71\t\r\n 72\t## Quick Start Guide\r\n 73\t\r\n 74\t### 1. Run a Single-Model Evaluation\r\n 75\t```bash\r\n 76\t# Test a specific model (defaults to gemini-2.5-flash)\r\n 77\tuv run agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py\r\n 78\t```\r\n 79\t\r\n 80\t### 2. Run Multi-Model Benchmarking\r\n 81\t```bash\r\n 82\t# Run evaluation across multiple models in parallel\r\n 83\tuv run agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py\r\n 84\t\r\n 85\t# Example: include an OpenRouter model (requires OPENROUTER_API_KEY)\r\n 86\tuv run agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py \\\r\n 87\t --models \"openrouter:anthropic/claude-3.7-sonnet,gemini-3-flash-preview\" \\\r\n 88\t --runs 5 \\\r\n 89\t --sequential\r\n 90\t```\r\n 91\t\r\n 92\t### 3. View Results in Local Dashboard (Recommended)\r\n 93\t```bash\r\n 94\t# Launch the interactive Streamlit dashboard to visualise your results\r\n 95\tmake leaderboard\r\n 96\t\r\n 97\t# Or run directly:\r\n 98\tuv run streamlit run agents_mcp_usage/evaluations/mermaid_evals/merbench_ui.py\r\n 99\t```\r\n 100\t\r\n 101\tThe local dashboard (`merbench_ui.py`) provides:\r\n 102\t- 📊 Interactive leaderboards and performance metrics\r\n 103\t- 📈 Pareto frontier visualisations (performance vs cost/tokens)\r\n 104\t- 🔍 Deep dive analysis with success rates and failure patterns\r\n 105\t- 💰 Configurable model costs\r\n 106\t- 🎯 Advanced filtering by provider, model, and test case\r\n 107\t\r\n 108\t### 4. Export Results for Web Dashboard (Optional)\r\n 109\t```bash\r\n 110\t# Convert CSV results to JSON format for the public Merbench website\r\n 111\tuv run agents_mcp_usage/evaluations/mermaid_evals/scripts/preprocess_merbench_data.py \\\r\n 112\t -i \"mermaid_eval_results/_combined_results.csv\" \\\r\n 113\t -o \"agents_mcp_usage/evaluations/mermaid_evals/results/_processed.json\"\r\n 114\t```\r\n 115\t\r\n 116\t### 5. Merge Results from Multiple Runs (Optional)\r\n 117\t```bash\r\n 118\t# Combine results from different machines or benchmark runs\r\n 119\tuv run agents_mcp_usage/evaluations/mermaid_evals/scripts/merge_benchmark_results.py \\\r\n 120\t -i results/run1.json results/run2.json \\\r\n 121\t -o results/merged.json \\\r\n 122\t --dedup keep-all \\\r\n 123\t --report merge_report.json\r\n 124\t```\r\n 125\t\r\n 126\t## Evaluation Task & Test Cases\r\n 127\t\r\n 128\tThe system challenges LLM agents to:\r\n 129\t1. **Identify and fix syntax errors** in invalid Mermaid diagrams\r\n 130\t2. **Utilise MCP servers** for validation and enhancement\r\n 131\t3. **Handle errors gracefully** with proper categorisation\r\n 132\t4. **Provide meaningful failure reasons** for debugging\r\n 133\t\r\n 134\t### Test Case Difficulty Levels\r\n 135\t\r\n 136\t1. **Easy** (2 syntax errors)\r\n 137\t - Incorrect node references (`GEMINI` vs `GEM`)\r\n 138\t - Minor naming inconsistencies\r\n 139\t - Example: `MCP --> GEMINI` where `GEMINI` is undefined\r\n 140\t\r\n 141\t2. **Medium** (7 syntax errors)\r\n 142\t - Invalid comment syntax (using `#` instead of `%%`)\r\n 143\t - Malformed arrows with spaces (`-- >` instead of `-->`)\r\n 144\t - Direction inconsistencies (`TB` vs `TD`)\r\n 145\t - Example: `LG -- > MCP` has invalid spacing in arrow\r\n 146\t\r\n 147\t3. **Hard** (Complex structural errors)\r\n 148\t - Multiple comment syntax errors\r\n 149\t - Arrow spacing issues throughout\r\n 150\t - Complex nested subgraph problems\r\n 151\t - Requires understanding of overall diagram structure\r\n 152\t\r\n 153\t### Example Error Types\r\n 154\t```mermaid\r\n 155\t%% Invalid examples from actual test cases:\r\n 156\tgraph LR\r\n 157\t %% Error 1: Space in arrow\r\n 158\t A -- > B %% Should be: A --> B\r\n 159\t \r\n 160\t %% Error 2: Undefined node reference\r\n 161\t C --> UNDEFINED_NODE %% Node doesn't exist\r\n 162\t \r\n 163\t %% Error 3: Wrong comment syntax\r\n 164\t # This is wrong %% Should use %%\r\n 165\t```\r\n 166\t\r\n 167\t## Output Schema & Metrics\r\n 168\t\r\n 169\t### MermaidOutput Schema\r\n 170\t```python\r\n",{"_670":1424,"_662":663,"_672":81},{"_731":1425,"_733":1426,"_735":1427,"_737":738,"_739":801,"_741":31,"_41":1420,"_742":1428,"_744":745,"_667":111,"_90":746}," 60\t\r\n 61\t### Configuration\r\n 62\tThe evaluation system uses Pydantic-AI and requires environment variables for API keys:\r\n 63\t```bash\r\n 64\t# Required for most models\r\n 65\texport GEMINI_API_KEY=\"your-key-here\"\r\n 66\t\r\n 67\t# Optional for specific providers/models\r\n 68\texport OPENAI_API_KEY=\"your-key-here\"\r\n 69\texport OPENROUTER_API_KEY=\"your-key-here\" # required for `openrouter:*` models\r\n 70\t```\r\n 71\t\r\n 72\t## Quick Start Guide\r\n 73\t\r\n 74\t### 1. Run a Single-Model Evaluation\r\n 75\t```bash\r\n 76\t# Test a specific model (defaults to gemini-2.5-flash)\r\n 77\tuv run agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py\r\n 78\t```\r\n 79\t\r\n 80\t### 2. Run Multi-Model Benchmarking\r\n 81\t```bash\r\n 82\t# Run evaluation across multiple models in parallel\r\n 83\tuv run agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py\r\n 84\t\r\n 85\t# Example: include an OpenRouter model (requires OPENROUTER_API_KEY)\r\n 86\tuv run agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py \\\r\n 87\t --models \"openrouter:anthropic/claude-3.7-sonnet,gemini-3-flash-preview\" \\\r\n 88\t --runs 5 \\\r\n 89\t --sequential\r\n 90\t```\r\n 91\t\r\n 92\t### 3. View Results in Local Dashboard (Recommended)\r\n 93\t```bash\r\n 94\t# Launch the interactive Streamlit dashboard to visualise your results\r\n 95\tmake leaderboard\r\n 96\t\r\n 97\t# Or run directly:\r\n 98\tuv run streamlit run agents_mcp_usage/evaluations/mermaid_evals/merbench_ui.py\r\n 99\t```\r\n 100\t\r\n 101\tThe local dashboard (`merbench_ui.py`) provides:\r\n 102\t- 📊 Interactive leaderboards and performance metrics\r\n 103\t- 📈 Pareto frontier visualisations (performance vs cost/tokens)\r\n 104\t- 🔍 Deep dive analysis with success rates and failure patterns\r\n 105\t- 💰 Configurable model costs\r\n 106\t- 🎯 Advanced filtering by provider, model, and test case\r\n 107\t\r\n 108\t### 4. Export Results for Web Dashboard (Optional)\r\n 109\t```bash\r\n 110\t# Convert CSV results to JSON format for the public Merbench website\r\n 111\tuv run agents_mcp_usage/evaluations/mermaid_evals/scripts/preprocess_merbench_data.py \\\r\n 112\t -i \"mermaid_eval_results/_combined_results.csv\" \\\r\n 113\t -o \"agents_mcp_usage/evaluations/mermaid_evals/results/_processed.json\"\r\n 114\t```\r\n 115\t\r\n 116\t### 5. Merge Results from Multiple Runs (Optional)\r\n 117\t```bash\r\n 118\t# Combine results from different machines or benchmark runs\r\n 119\tuv run agents_mcp_usage/evaluations/mermaid_evals/scripts/merge_benchmark_results.py \\\r\n 120\t -i results/run1.json results/run2.json \\\r\n 121\t -o results/merged.json \\\r\n 122\t --dedup keep-all \\\r\n 123\t --report merge_report.json\r\n 124\t```\r\n 125\t\r\n 126\t## Evaluation Task & Test Cases\r\n 127\t\r\n 128\tThe system challenges LLM agents to:\r\n 129\t1. **Identify and fix syntax errors** in invalid Mermaid diagrams\r\n 130\t2. **Utilise MCP servers** for validation and enhancement\r\n 131\t3. **Handle errors gracefully** with proper categorisation\r\n 132\t4. **Provide meaningful failure reasons** for debugging\r\n 133\t\r\n 134\t### Test Case Difficulty Levels\r\n 135\t\r\n 136\t1. **Easy** (2 syntax errors)\r\n 137\t - Incorrect node references (`GEMINI` vs `GEM`)\r\n 138\t - Minor naming inconsistencies\r\n 139\t - Example: `MCP --> GEMINI` where `GEMINI` is undefined\r\n 140\t\r\n 141\t2. **Medium** (7 syntax errors)\r\n 142\t - Invalid comment syntax (using `#` instead of `%%`)\r\n 143\t - Malformed arrows with spaces (`-- >` instead of `-->`)\r\n 144\t - Direction inconsistencies (`TB` vs `TD`)\r\n 145\t - Example: `LG -- > MCP` has invalid spacing in arrow\r\n 146\t\r\n 147\t3. **Hard** (Complex structural errors)\r\n 148\t - Multiple comment syntax errors\r\n 149\t - Arrow spacing issues throughout\r\n 150\t - Complex nested subgraph problems\r\n 151\t - Requires understanding of overall diagram structure\r\n 152\t\r\n 153\t### Example Error Types\r\n 154\t```mermaid\r\n 155\t%% Invalid examples from actual test cases:\r\n 156\tgraph LR\r\n 157\t %% Error 1: Space in arrow\r\n 158\t A -- > B %% Should be: A --> B\r\n 159\t \r\n 160\t %% Error 2: Undefined node reference\r\n 161\t C --> UNDEFINED_NODE %% Node doesn't exist\r\n 162\t \r\n 163\t %% Error 3: Wrong comment syntax\r\n 164\t # This is wrong %% Should use %%\r\n 165\t```\r\n 166\t\r\n 167\t## Output Schema & Metrics\r\n 168\t\r\n 169\t### MermaidOutput Schema\r\n 170\t```python\r\n","/bin/bash -lc 'nl -ba agents_mcp_usage/evaluations/mermaid_evals/README.md | sed -n '\"'\"'60,170p'\"'\"''",[1429],"58589",{"_733":1430,"_297":749,"_216":1394,"_90":751},"nl -ba agents_mcp_usage/evaluations/mermaid_evals/README.md | sed -n '60,170p'",{"_670":1432,"_662":663,"_672":81},{"_731":-5,"_733":1426,"_735":1433,"_737":738,"_739":-5,"_741":-5,"_41":1420,"_742":1428,"_744":745,"_667":755,"_90":746},[1434],{"_733":1430,"_297":749,"_216":1394,"_90":751},{"_670":1436,"_662":663,"_672":81},{"_759":1437,"_722":1420,"_41":-5,"_297":761,"_762":-5,"_90":763},"{\"cmd\":\"nl -ba agents_mcp_usage/evaluations/mermaid_evals/README.md | sed -n '60,170p'\"}",{"_670":1439,"_662":663,"_672":81},{"_673":-5,"_691":1440,"_41":1441,"_694":1442,"_90":696},"gAAAAABpaAi1Ibb_7p7eHxccpW_dPW924IbC79ATafZRz-A-LN706TrGtZHXy7KgHyPzTybEhaMr7TLzo-A63roj3407UgxBDPSfhD1zjXl0dQQZpMi6Ny3itzjSKdfiQN1ndM0__KXNCtbYbjrIkuvx_i-pXCpH8bq0xd80Eu-Apo49nSZC7oq17cSX1pkOZ-MAi2FezGILhSsJ8P2w976GIlqLB7bK-yPyBYGdRAJkrJyNQ1qoq_x3iklIyshV7eh8LqOhffE0V0CUFqZ5zgBq7HfYdsu8SlV3P6q0xrkDgbVW3w7Q2exB5GwRmRiZiKqbSmh-YSRbI5WJshRgH31Mn1qchXgGHZwzGfQk3hE_W7K8BXtdQ7SOvm-IyD_5GiqF1VX6knSXd9ir8Yil-saRl2Tg0u0eUMayss2NUvQO-WVJ8kiHrg-l1WUHagSgLnvaosmriRVGf8aeUlx0NuDAdvwd4W4qlkYmCAR76TWTS3QD0MQvFVRPy7PhAWhhskuAvTM6GfeUGWf_w8a__RMWa9-XrYBi7O_YV3juFtSmaaoLK0pMdbubTDic5F--huZ2RINiyuMD4Pwl5GGihhZXuol9jTFKiHy5lF_IqGotDOJLS9tZ9U1XM9HhXoBnIYpLSY6-koL_L6DIwHH1v--5Pd3R7c2xRJYUyjInc8PgYWNx9N7bPuMhlOLIirZe2lKV9rIwgMnT4ZdsAtBlp4GivxiUcphuEbjIPIYmR1iM7fVE3sbPe2uFItENLQCdbvD8S3fXxbtH7ZYZ2Wb4AP-UfDG5uuIWHIrr3IXx95TWMifr61lMtqcLgafZ5bX1JgFg1Vg0pqKIOi5xEWXXU0gKYOf1KkMqbYmGTvht-0KSpvsejROwaElMOGBZIRfYX0dBWmrTUJ7NpEpEp44ZQCvOO611-TsRQHAC2D9dGUPOVFpmuCjZqZk=","legacy_responseItem_reasoning_25",[1443],{"_45":1444,"_90":700},"**Adding README for mermaid evals and openrouter env**",{"_670":1446,"_662":663,"_672":81},{"_673":1447,"_41":1448,"_694":1449,"_90":696},[1444],"legacy_reasoning_24",[],{"_670":1451,"_662":663,"_672":81},{"_673":1452,"_41":1448,"_694":1453,"_90":696},[],[],{"_670":1455,"_662":663,"_672":81},{"_722":1456,"_724":1457,"_90":726},"call_XlHPRiDxaGF5xjxnR60tnobq",{"_408":1458,"_728":-5},"Chunk ID: 873425\nWall time: 0.0513 seconds\nProcess exited with code 0\nOriginal token count: 2273\nOutput:\n 240\t if isinstance(response, chat.ChatCompletion):\r\n 241\t data = response.model_dump()\r\n 242\t\r\n 243\t # Fix invalid tool_calls payloads (e.g. `arguments: null`)\r\n 244\t try:\r\n 245\t choices = data.get(\"choices\")\r\n 246\t if isinstance(choices, list):\r\n 247\t for choice in choices:\r\n 248\t if not isinstance(choice, dict):\r\n 249\t continue\r\n 250\t message = choice.get(\"message\")\r\n 251\t if not isinstance(message, dict):\r\n 252\t continue\r\n 253\t tool_calls = message.get(\"tool_calls\")\r\n 254\t if not isinstance(tool_calls, list):\r\n 255\t continue\r\n 256\t for call in tool_calls:\r\n 257\t if not isinstance(call, dict):\r\n 258\t continue\r\n 259\t if call.get(\"type\") != \"function\":\r\n 260\t continue\r\n 261\t fn = call.get(\"function\")\r\n 262\t if not isinstance(fn, dict):\r\n 263\t fn = {}\r\n 264\t call[\"function\"] = fn\r\n 265\t args = fn.get(\"arguments\")\r\n 266\t if args is None:\r\n 267\t fn[\"arguments\"] = \"{}\"\r\n 268\t elif not isinstance(args, str):\r\n 269\t fn[\"arguments\"] = json.dumps(args)\r\n 270\t except Exception:\r\n 271\t # Best-effort only; fall through to default handling.\r\n 272\t pass\r\n 273\t\r\n 274\t try:\r\n 275\t response = chat.ChatCompletion.model_validate(data)\r\n 276\t except ValidationError:\r\n 277\t # If sanitation didn't help, let the base model raise a\r\n 278\t # helpful UnexpectedModelBehavior.\r\n 279\t pass\r\n 280\t\r\n 281\t return super()._process_response(response)\r\n 282\t\r\n 283\t config = PROVIDER_CONFIGS[\"openrouter\"]\r\n 284\t provider_kwargs = provider_kwargs or {}\r\n 285\t\r\n 286\t api_key = provider_kwargs.get(\"api_key\") or os.getenv(config[\"env_var\"])\r\n 287\t if not api_key:\r\n 288\t raise ValueError(\r\n 289\t \"OpenRouter API key not configured. \"\r\n 290\t \"Set OPENROUTER_API_KEY or pass provider_kwargs={'api_key': '...'}\"\r\n 291\t )\r\n 292\t\r\n 293\t app_url = provider_kwargs.get(\"app_url\") or os.getenv(config[\"env_vars\"][\"app_url\"])\r\n 294\t app_title = provider_kwargs.get(\"app_title\") or os.getenv(config[\"env_vars\"][\"app_title\"])\r\n 295\t\r\n 296\t # OpenRouter app attribution uses HTTP headers:\r\n 297\t # - HTTP-Referer: your app URL\r\n 298\t # - X-Title: your app name\r\n 299\t # See: https://openrouter.ai/docs/app-attribution\r\n 300\t default_headers: dict[str, str] = {}\r\n 301\t if app_url:\r\n 302\t default_headers[\"HTTP-Referer\"] = app_url\r\n 303\t if app_title:\r\n 304\t default_headers[\"X-Title\"] = app_title\r\n 305\t\r\n 306\t base_url = provider_kwargs.get(\"base_url\") or \"https://openrouter.ai/api/v1\"\r\n 307\t openai_client = AsyncOpenAI(\r\n 308\t base_url=base_url,\r\n 309\t api_key=api_key,\r\n 310\t default_headers=default_headers or None,\r\n 311\t )\r\n 312\t provider_instance = OpenRouterProvider(openai_client=openai_client)\r\n 313\t\r\n 314\t return OpenRouterPatchedChatModel(model_name, provider=provider_instance)\r\n 315\t\r\n 316\t\r\n 317\tdef create_model(\r\n 318\t model_string: str,\r\n 319\t model_settings: Optional[Dict[str, Any]] = None,\r\n 320\t provider_kwargs: Optional[Dict[str, Any]] = None\r\n 321\t) -> Union[Model, str]:\r\n 322\t \"\"\"\r\n 323\t Create a model instance from a model string.\r\n 324\t \r\n 325\t This function:\r\n 326\t 1. Returns the string directly for providers PydanticAI handles natively\r\n 327\t 2. Handles special cases like Bedrock and OpenAI-compatible providers\r\n 328\t 3. Falls back to returning the string for unknown providers\r\n 329\t \r\n 330\t Args:\r\n 331\t model_string: Model identifier\r\n 332\t model_settings: Optional model-specific settings\r\n 333\t provider_kwargs: Optional provider-specific kwargs (e.g., azure_endpoint)\r\n 334\t \r\n 335\t Returns:\r\n 336\t Either a Model instance or the original string for PydanticAI to handle\r\n 337\t \"\"\"\r\n 338\t # First, normalise any OpenAI models to use the Responses API\r\n 339\t model_string = ensure_openai_responses_model(model_string)\r\n 340\t\r\n 341\t provider, model_name = parse_model_string(model_string)\r\n 342\t \r\n 343\t # No provider prefix - let PydanticAI handle it\r\n 344\t if provider is None:\r\n 345\t return model_string\r\n 346\t \r\n 347\t # Standard providers that PydanticAI auto-detects\r\n 348\t # Note: OpenAI models have been converted to \"openai-responses:*\" above\r\n 349\t if provider in [\"google\", \"anthropic\"]:\r\n 350\t return model_string\r\n 351\t \r\n 352\t # Providers with native PydanticAI support\r\n 353\t if provider in NATIVE_PROVIDERS:\r\n 354\t # Special validation for Azure\r\n 355\t if provider == \"azure\" and provider_kwargs:\r\n 356\t required = [\"azure_endpoint\", \"api_version\"]\r\n 357\t missing = [k for k in required if k not in provider_kwargs]\r\n 358\t if missing:\r\n 359\t raise ValueError(\r\n 360\t f\"Azure provider requires {missing} in provider_kwargs. \"\r\n 361\t f\"Alternatively, let PydanticAI handle it by using the string directly.\"\r\n 362\t )\r\n 363\t # Return the string - PydanticAI will handle it\r\n 364\t return model_string\r\n 365\t \r\n 366\t # Handle special cases\r\n 367\t if provider in PROVIDER_CONFIGS:\r\n 368\t config = PROVIDER_CONFIGS[provider]\r\n 369\t handler = config.get(\"handler\")\r\n 370\t \r\n 371\t if handler == \"bedrock_handler\":\r\n 372\t return handle_bedrock_model(model_name, provider_kwargs)\r\n 373\t elif handler == \"openai_compatible_handler\":\r\n 374\t return handle_openai_compatible(provider, model_name, provider_kwargs)\r\n 375\t elif handler == \"openrouter_handler\":\r\n 376\t return handle_openrouter_model(model_name, provider_kwargs)\r\n 377\t \r\n 378\t # Unknown provider - return string and let PydanticAI try\r\n 379\t return model_string\r\n 380\t\r\n 381\t\r\n 382\tdef create_agent(\r\n 383\t model: Union[str, Model],\r\n 384\t mcp_servers=None,\r\n 385\t model_settings: Optional[Dict[str, Any]] = None,\r\n 386\t provider_kwargs: Optional[Dict[str, Any]] = None,\r\n 387\t **agent_kwargs\r\n 388\t) -> Agent:\r\n 389\t \"\"\"\r\n 390\t Create an Agent with proper model handling.\r\n 391\t \r\n 392\t This is a convenience function that:\r\n 393\t 1. Converts model strings to appropriate Model instances when needed\r\n 394\t 2. Passes through Model instances unchanged\r\n 395\t 3. Leverages PydanticAI's native provider support\r\n 396\t \r\n 397\t Args:\r\n 398\t model: Model string or Model instance\r\n 399\t mcp_servers: Optional MCP servers\r\n 400\t model_settings: Optional model settings\r\n 401\t provider_kwargs: Optional provider-specific kwargs\r\n 402\t **agent_kwargs: Additional Agent kwargs\r\n 403\t \r\n 404\t Returns:\r\n 405\t Configured Agent instance\r\n 406\t \"\"\"\r\n 407\t if isinstance(model, str):\r\n 408\t base_model, reasoning_effort = extract_reasoning_effort(model)\r\n 409\t\r\n 410\t # Ensure we have a dict to attach model-level settings\r\n 411\t if model_settings is None:\r\n 412\t model_settings = {}\r\n 413\t\r\n 414\t # Only apply reasoning hint to OpenAI Responses-capable models\r\n 415\t provider, _ = parse_model_string(base_model)\r\n 416\t if (\r\n 417\t reasoning_effort\r\n 418\t and isinstance(model_settings, dict)\r\n 419\t and provider in {\"openai\", \"openai-responses\"}\r\n 420\t and \"openai_reasoning_effort\" not in model_settings\r\n 421\t ):\r\n 422\t model_settings[\"openai_reasoning_effort\"] = reasoning_effort\r\n 423\t\r\n 424\t model = create_model(base_model, model_settings, provider_kwargs)\r\n 425\t\r\n 426\t return Agent(\r\n 427\t model,\r\n 428\t mcp_servers=mcp_servers,\r\n 429\t model_settings=model_settings if isinstance(model, str) else None,\r\n 430\t **agent_kwargs\r\n 431\t )\r\n",{"_670":1460,"_662":663,"_672":81},{"_731":1461,"_733":1462,"_735":1463,"_737":738,"_739":740,"_741":31,"_41":1456,"_742":1464,"_744":745,"_667":111,"_90":746}," 240\t if isinstance(response, chat.ChatCompletion):\r\n 241\t data = response.model_dump()\r\n 242\t\r\n 243\t # Fix invalid tool_calls payloads (e.g. `arguments: null`)\r\n 244\t try:\r\n 245\t choices = data.get(\"choices\")\r\n 246\t if isinstance(choices, list):\r\n 247\t for choice in choices:\r\n 248\t if not isinstance(choice, dict):\r\n 249\t continue\r\n 250\t message = choice.get(\"message\")\r\n 251\t if not isinstance(message, dict):\r\n 252\t continue\r\n 253\t tool_calls = message.get(\"tool_calls\")\r\n 254\t if not isinstance(tool_calls, list):\r\n 255\t continue\r\n 256\t for call in tool_calls:\r\n 257\t if not isinstance(call, dict):\r\n 258\t continue\r\n 259\t if call.get(\"type\") != \"function\":\r\n 260\t continue\r\n 261\t fn = call.get(\"function\")\r\n 262\t if not isinstance(fn, dict):\r\n 263\t fn = {}\r\n 264\t call[\"function\"] = fn\r\n 265\t args = fn.get(\"arguments\")\r\n 266\t if args is None:\r\n 267\t fn[\"arguments\"] = \"{}\"\r\n 268\t elif not isinstance(args, str):\r\n 269\t fn[\"arguments\"] = json.dumps(args)\r\n 270\t except Exception:\r\n 271\t # Best-effort only; fall through to default handling.\r\n 272\t pass\r\n 273\t\r\n 274\t try:\r\n 275\t response = chat.ChatCompletion.model_validate(data)\r\n 276\t except ValidationError:\r\n 277\t # If sanitation didn't help, let the base model raise a\r\n 278\t # helpful UnexpectedModelBehavior.\r\n 279\t pass\r\n 280\t\r\n 281\t return super()._process_response(response)\r\n 282\t\r\n 283\t config = PROVIDER_CONFIGS[\"openrouter\"]\r\n 284\t provider_kwargs = provider_kwargs or {}\r\n 285\t\r\n 286\t api_key = provider_kwargs.get(\"api_key\") or os.getenv(config[\"env_var\"])\r\n 287\t if not api_key:\r\n 288\t raise ValueError(\r\n 289\t \"OpenRouter API key not configured. \"\r\n 290\t \"Set OPENROUTER_API_KEY or pass provider_kwargs={'api_key': '...'}\"\r\n 291\t )\r\n 292\t\r\n 293\t app_url = provider_kwargs.get(\"app_url\") or os.getenv(config[\"env_vars\"][\"app_url\"])\r\n 294\t app_title = provider_kwargs.get(\"app_title\") or os.getenv(config[\"env_vars\"][\"app_title\"])\r\n 295\t\r\n 296\t # OpenRouter app attribution uses HTTP headers:\r\n 297\t # - HTTP-Referer: your app URL\r\n 298\t # - X-Title: your app name\r\n 299\t # See: https://openrouter.ai/docs/app-attribution\r\n 300\t default_headers: dict[str, str] = {}\r\n 301\t if app_url:\r\n 302\t default_headers[\"HTTP-Referer\"] = app_url\r\n 303\t if app_title:\r\n 304\t default_headers[\"X-Title\"] = app_title\r\n 305\t\r\n 306\t base_url = provider_kwargs.get(\"base_url\") or \"https://openrouter.ai/api/v1\"\r\n 307\t openai_client = AsyncOpenAI(\r\n 308\t base_url=base_url,\r\n 309\t api_key=api_key,\r\n 310\t default_headers=default_headers or None,\r\n 311\t )\r\n 312\t provider_instance = OpenRouterProvider(openai_client=openai_client)\r\n 313\t\r\n 314\t return OpenRouterPatchedChatModel(model_name, provider=provider_instance)\r\n 315\t\r\n 316\t\r\n 317\tdef create_model(\r\n 318\t model_string: str,\r\n 319\t model_settings: Optional[Dict[str, Any]] = None,\r\n 320\t provider_kwargs: Optional[Dict[str, Any]] = None\r\n 321\t) -> Union[Model, str]:\r\n 322\t \"\"\"\r\n 323\t Create a model instance from a model string.\r\n 324\t \r\n 325\t This function:\r\n 326\t 1. Returns the string directly for providers PydanticAI handles natively\r\n 327\t 2. Handles special cases like Bedrock and OpenAI-compatible providers\r\n 328\t 3. Falls back to returning the string for unknown providers\r\n 329\t \r\n 330\t Args:\r\n 331\t model_string: Model identifier\r\n 332\t model_settings: Optional model-specific settings\r\n 333\t provider_kwargs: Optional provider-specific kwargs (e.g., azure_endpoint)\r\n 334\t \r\n 335\t Returns:\r\n 336\t Either a Model instance or the original string for PydanticAI to handle\r\n 337\t \"\"\"\r\n 338\t # First, normalise any OpenAI models to use the Responses API\r\n 339\t model_string = ensure_openai_responses_model(model_string)\r\n 340\t\r\n 341\t provider, model_name = parse_model_string(model_string)\r\n 342\t \r\n 343\t # No provider prefix - let PydanticAI handle it\r\n 344\t if provider is None:\r\n 345\t return model_string\r\n 346\t \r\n 347\t # Standard providers that PydanticAI auto-detects\r\n 348\t # Note: OpenAI models have been converted to \"openai-responses:*\" above\r\n 349\t if provider in [\"google\", \"anthropic\"]:\r\n 350\t return model_string\r\n 351\t \r\n 352\t # Providers with native PydanticAI support\r\n 353\t if provider in NATIVE_PROVIDERS:\r\n 354\t # Special validation for Azure\r\n 355\t if provider == \"azure\" and provider_kwargs:\r\n 356\t required = [\"azure_endpoint\", \"api_version\"]\r\n 357\t missing = [k for k in required if k not in provider_kwargs]\r\n 358\t if missing:\r\n 359\t raise ValueError(\r\n 360\t f\"Azure provider requires {missing} in provider_kwargs. \"\r\n 361\t f\"Alternatively, let PydanticAI handle it by using the string directly.\"\r\n 362\t )\r\n 363\t # Return the string - PydanticAI will handle it\r\n 364\t return model_string\r\n 365\t \r\n 366\t # Handle special cases\r\n 367\t if provider in PROVIDER_CONFIGS:\r\n 368\t config = PROVIDER_CONFIGS[provider]\r\n 369\t handler = config.get(\"handler\")\r\n 370\t \r\n 371\t if handler == \"bedrock_handler\":\r\n 372\t return handle_bedrock_model(model_name, provider_kwargs)\r\n 373\t elif handler == \"openai_compatible_handler\":\r\n 374\t return handle_openai_compatible(provider, model_name, provider_kwargs)\r\n 375\t elif handler == \"openrouter_handler\":\r\n 376\t return handle_openrouter_model(model_name, provider_kwargs)\r\n 377\t \r\n 378\t # Unknown provider - return string and let PydanticAI try\r\n 379\t return model_string\r\n 380\t\r\n 381\t\r\n 382\tdef create_agent(\r\n 383\t model: Union[str, Model],\r\n 384\t mcp_servers=None,\r\n 385\t model_settings: Optional[Dict[str, Any]] = None,\r\n 386\t provider_kwargs: Optional[Dict[str, Any]] = None,\r\n 387\t **agent_kwargs\r\n 388\t) -> Agent:\r\n 389\t \"\"\"\r\n 390\t Create an Agent with proper model handling.\r\n 391\t \r\n 392\t This is a convenience function that:\r\n 393\t 1. Converts model strings to appropriate Model instances when needed\r\n 394\t 2. Passes through Model instances unchanged\r\n 395\t 3. Leverages PydanticAI's native provider support\r\n 396\t \r\n 397\t Args:\r\n 398\t model: Model string or Model instance\r\n 399\t mcp_servers: Optional MCP servers\r\n 400\t model_settings: Optional model settings\r\n 401\t provider_kwargs: Optional provider-specific kwargs\r\n 402\t **agent_kwargs: Additional Agent kwargs\r\n 403\t \r\n 404\t Returns:\r\n 405\t Configured Agent instance\r\n 406\t \"\"\"\r\n 407\t if isinstance(model, str):\r\n 408\t base_model, reasoning_effort = extract_reasoning_effort(model)\r\n 409\t\r\n 410\t # Ensure we have a dict to attach model-level settings\r\n 411\t if model_settings is None:\r\n 412\t model_settings = {}\r\n 413\t\r\n 414\t # Only apply reasoning hint to OpenAI Responses-capable models\r\n 415\t provider, _ = parse_model_string(base_model)\r\n 416\t if (\r\n 417\t reasoning_effort\r\n 418\t and isinstance(model_settings, dict)\r\n 419\t and provider in {\"openai\", \"openai-responses\"}\r\n 420\t and \"openai_reasoning_effort\" not in model_settings\r\n 421\t ):\r\n 422\t model_settings[\"openai_reasoning_effort\"] = reasoning_effort\r\n 423\t\r\n 424\t model = create_model(base_model, model_settings, provider_kwargs)\r\n 425\t\r\n 426\t return Agent(\r\n 427\t model,\r\n 428\t mcp_servers=mcp_servers,\r\n 429\t model_settings=model_settings if isinstance(model, str) else None,\r\n 430\t **agent_kwargs\r\n 431\t )\r\n","/bin/bash -lc 'nl -ba agents_mcp_usage/factory/model_factory.py | sed -n '\"'\"'240,520p'\"'\"''",[1465],"89693",{"_733":1466,"_297":1467,"_216":1468,"_90":751},"nl -ba agents_mcp_usage/factory/model_factory.py | sed -n '240,520p'","model_factory.py","agents_mcp_usage/factory/model_factory.py",{"_670":1470,"_662":663,"_672":81},{"_731":-5,"_733":1462,"_735":1471,"_737":738,"_739":-5,"_741":-5,"_41":1456,"_742":1464,"_744":745,"_667":755,"_90":746},[1472],{"_733":1466,"_297":1467,"_216":1468,"_90":751},{"_670":1474,"_662":663,"_672":81},{"_759":1475,"_722":1456,"_41":-5,"_297":761,"_762":-5,"_90":763},"{\"cmd\":\"nl -ba agents_mcp_usage/factory/model_factory.py | sed -n '240,520p'\"}",{"_670":1477,"_662":663,"_672":81},{"_673":-5,"_691":1478,"_41":1479,"_694":1480,"_90":696},"gAAAAABpaAiz3YQ50byHtvnP23CSn21NJM1FjYsGaTW2hMalT-NuJek2trPVso23s0NuEltrO0EtUXXSU79mKFZtVuoi_X4rcgMR004MZ4aOfpvPtl5rv-J5CzzFlZ6oO9smiK8S3tSc6SlyHqlvWyt5w7Q3AKXv9MsKZ4QGLDTb2GXQecSijiWnLqKVFhOSwb_WD_-y-FtiUqRFSLp3NaLcn-WncaAIiCLnCQhXn2R1KvOdgJLXsDTCC2gQMuqE8ESVNTMHraR5RTOzbNUt5Buh4lwdjWQgCWMvxz_F2BFXfMU3tH4FJk0CRsi0GCX1RCGocwlcQ7GjJKfv3tZ6HK7sz6NWWGwEmQEek5xYuZrdabbkpelbsKdXtttlo4wfA4nTxffjXqMTZ3GHO-ZupdsVoNDA1viICKWxY2kGEz-cBV2AhdVdAonEZu50_tvgTPE3BeOvU891TA0SFRXW5IFUr0P2fr8s8vGcc6wMMYj-TFSgCAmaTeg9i1DdioI12yDfB3_F3i8-3tJx0Lw1P8HMZjssu1s2a2FvYJE3mpyqzXMixMdDGmQHfY32fN_2dtfTlPxr5nOhgdF_MkOjqrfQsBHFtwSFobnP4MFf7lyCU5uNX-QzQO0Sw7eKPYg0JKLDJMGhjfi_9j5SqUYOPomEfiILf2EDv0DovOxIp5W--CbpSE5WM4REJQRb91gMZFGqp9lZRzAYaA7GozDT6nRgv8-JQFuxzlCE__SY3cS-RGx-NKZ85SYvfr8nUZZpSjUGd4X93YeSDunSA81MPaSrFHJbzfBFN27WPpssQOvZQ-nqBxd-BXv_H6ZUWoP96lF2B9r7E2P44647_Wi7dUvEvJxRZCHB3PcKqF7Ji0yEQlg19fMibusTg1rgQzBOsVxZ23eF8xhZ","legacy_responseItem_reasoning_23",[1481],{"_45":1482,"_90":700},"**Requesting more code lines**",{"_670":1484,"_662":663,"_672":81},{"_673":1485,"_41":1486,"_694":1487,"_90":696},[1482],"legacy_reasoning_22",[],{"_670":1489,"_662":663,"_672":81},{"_673":1490,"_41":1486,"_694":1491,"_90":696},[],[],{"_670":1493,"_662":663,"_672":81},{"_722":1494,"_724":1495,"_90":726},"call_Y05no9GyFU2G9UgFNMy5P71b",{"_408":1496,"_728":-5},"Chunk ID: 6c504a\nWall time: 0.0505 seconds\nProcess exited with code 0\nOriginal token count: 2569\nOutput:\n 1\t\"\"\"\r\n 2\tModel Factory for PydanticAI\r\n 3\t\r\n 4\tThis module provides a centralized way to create models across different providers,\r\n 5\tleveraging PydanticAI's built-in provider support and handling special cases.\r\n 6\t\"\"\"\r\n 7\t\r\n 8\timport os\r\n 9\timport re\r\n 10\tfrom typing import Any, Dict, Optional, Union\r\n 11\tfrom pydantic_ai import Agent, InlineDefsJsonSchemaTransformer\r\n 12\tfrom pydantic_ai.models import Model\r\n 13\tfrom pydantic_ai.models.openai import OpenAIResponsesModel\r\n 14\tfrom pydantic_ai.profiles.openai import OpenAIModelProfile\r\n 15\tfrom pydantic_ai.providers.openai import OpenAIProvider\r\n 16\t\r\n 17\t\r\n 18\t# Providers that PydanticAI handles natively with provider:model syntax.\r\n 19\t#\r\n 20\t# NOTE:\r\n 21\t# - `openrouter:*` is intentionally **not** treated as native in this repo.\r\n 22\t# We construct an explicit OpenRouter provider + OpenAI-compatible model so we\r\n 23\t# can control configuration and avoid relying on implicit provider detection.\r\n 24\tNATIVE_PROVIDERS = {\r\n 25\t \"deepseek\", \"github\", \"grok\", \"azure\",\r\n 26\t \"fireworks\", \"together\", \"heroku\"\r\n 27\t}\r\n 28\t\r\n 29\t# Special cases that need custom handling\r\n 30\tPROVIDER_CONFIGS = {\r\n 31\t \"bedrock\": {\r\n 32\t # Bedrock uses a different model class\r\n 33\t \"handler\": \"bedrock_handler\",\r\n 34\t \"env_vars\": {\r\n 35\t \"region\": \"AWS_REGION\",\r\n 36\t \"profile\": \"AWS_PROFILE\"\r\n 37\t }\r\n 38\t },\r\n 39\t \"ollama\": {\r\n 40\t # Ollama is OpenAI-compatible but needs custom base URL\r\n 41\t \"handler\": \"openai_compatible_handler\",\r\n 42\t \"base_url\": \"http://localhost:11434/v1\",\r\n 43\t \"api_key\": \"not-needed\",\r\n 44\t \"profile\": {\r\n 45\t \"json_schema_transformer\": InlineDefsJsonSchemaTransformer,\r\n 46\t \"openai_supports_strict_tool_definition\": False\r\n 47\t }\r\n 48\t },\r\n 49\t \"perplexity\": {\r\n 50\t # Perplexity is OpenAI-compatible with custom base URL\r\n 51\t \"handler\": \"openai_compatible_handler\",\r\n 52\t \"base_url\": \"https://api.perplexity.ai\",\r\n 53\t \"env_var\": \"PERPLEXITY_API_KEY\"\r\n 54\t },\r\n 55\t \"openrouter\": {\r\n 56\t # OpenRouter has first-class support in PydanticAI, but we construct the\r\n 57\t # provider/model explicitly so configuration is controlled by this repo.\r\n 58\t \"handler\": \"openrouter_handler\",\r\n 59\t \"env_var\": \"OPENROUTER_API_KEY\",\r\n 60\t \"env_vars\": {\r\n 61\t \"app_url\": \"OPENROUTER_APP_URL\",\r\n 62\t \"app_title\": \"OPENROUTER_APP_TITLE\",\r\n 63\t },\r\n 64\t }\r\n 65\t}\r\n 66\t\r\n 67\t\r\n 68\tdef ensure_openai_responses_model(model_string: str) -> str:\r\n 69\t \"\"\"Ensure OpenAI models use the Responses API.\r\n 70\t\r\n 71\t This helper normalises any OpenAI model identifier to the\r\n 72\t `openai-responses:` provider namespace so that PydanticAI\r\n 73\t will use the OpenAI Responses API rather than Chat Completions.\r\n 74\t\r\n 75\t Examples:\r\n 76\t \"gpt-5\" -> \"openai-responses:gpt-5\"\r\n 77\t \"openai:gpt-5-mini\" -> \"openai-responses:gpt-5-mini\"\r\n 78\t \"openai-responses:gpt-5\" -> \"openai-responses:gpt-5\" (unchanged)\r\n 79\t \"\"\"\r\n 80\t # Avoid double-prefixing if already using Responses API directly\r\n 81\t if model_string.startswith(\"openai-responses:\"):\r\n 82\t return model_string\r\n 83\t\r\n 84\t provider, model_name = parse_model_string(model_string)\r\n 85\t if provider == \"openai\":\r\n 86\t return f\"openai-responses:{model_name}\"\r\n 87\t\r\n 88\t return model_string\r\n 89\t\r\n 90\t\r\n 91\t# Accept optional reasoning hints for models that support it\r\n 92\tREASONING_SUFFIX_RE = re.compile(r\"\\s*\\((low|medium|high|none|minimal|xhigh)\\)\\s*$\", re.IGNORECASE)\r\n 93\t\r\n 94\t\r\n 95\tdef extract_reasoning_effort(model_string: str) -> tuple[str, Optional[str]]:\r\n 96\t \"\"\"Strip trailing reasoning hint like \"(medium)\" or \"(none)\" from model string.\r\n 97\t\r\n 98\t Returns the base model string and the effort level (lowercased) if present.\r\n 99\t \"\"\"\r\n 100\t\r\n 101\t match = REASONING_SUFFIX_RE.search(model_string)\r\n 102\t if not match:\r\n 103\t return model_string.strip(), None\r\n 104\t\r\n 105\t effort = match.group(1).lower()\r\n 106\t base = REASONING_SUFFIX_RE.sub(\"\", model_string).strip()\r\n 107\t return base, effort\r\n 108\t\r\n 109\t\r\n 110\tdef parse_model_string(model_string: str) -> tuple[Optional[str], str]:\r\n 111\t \"\"\"\r\n 112\t Parse a model string to extract provider and model name.\r\n 113\t \r\n 114\t Args:\r\n 115\t model_string: Model identifier (e.g., \"openai:gpt-4\", \"deepseek:deepseek-chat\", \"gemini-2.5-pro\")\r\n 116\t \r\n 117\t Returns:\r\n 118\t Tuple of (provider, model_name)\r\n 119\t \"\"\"\r\n 120\t if \":\" in model_string:\r\n 121\t provider, model_name = model_string.split(\":\", 1)\r\n 122\t return provider, model_name\r\n 123\t \r\n 124\t # Check for known prefixes without colon\r\n 125\t if model_string.startswith(\"gemini-\"):\r\n 126\t return \"google\", model_string\r\n 127\t elif model_string.startswith(\"gpt-\") or model_string.startswith(\"o1-\") or model_string.startswith(\"o3-\") or model_string.startswith(\"o4-\"):\r\n 128\t return \"openai\", model_string\r\n 129\t elif model_string.startswith(\"claude-\"):\r\n 130\t return \"anthropic\", model_string\r\n 131\t \r\n 132\t # Default to no specific provider\r\n 133\t return None, model_string\r\n 134\t\r\n 135\t\r\n 136\tdef handle_bedrock_model(\r\n 137\t model_name: str,\r\n 138\t provider_kwargs: Optional[Dict[str, Any]] = None\r\n 139\t) -> Model:\r\n 140\t \"\"\"Handle AWS Bedrock models which require special treatment.\"\"\"\r\n 141\t from pydantic_ai.models.bedrock import BedrockConverseModel\r\n 142\t from pydantic_ai.providers.bedrock import BedrockProvider\r\n 143\t \r\n 144\t config = PROVIDER_CONFIGS[\"bedrock\"]\r\n 145\t provider_kwargs = provider_kwargs or {}\r\n 146\t \r\n 147\t region = provider_kwargs.get(\"region_name\") or os.getenv(config[\"env_vars\"][\"region\"], \"us-east-1\")\r\n 148\t profile = provider_kwargs.get(\"profile_name\") or os.getenv(config[\"env_vars\"][\"profile\"], \"default\")\r\n 149\t \r\n 150\t provider_instance = BedrockProvider(\r\n 151\t region_name=region,\r\n 152\t profile_name=profile\r\n 153\t )\r\n 154\t \r\n 155\t return BedrockConverseModel(\r\n 156\t model_name,\r\n 157\t provider=provider_instance\r\n 158\t )\r\n 159\t\r\n 160\t\r\n 161\tdef handle_openai_compatible(\r\n 162\t provider: str,\r\n 163\t model_name: str,\r\n 164\t provider_kwargs: Optional[Dict[str, Any]] = None\r\n 165\t) -> Model:\r\n 166\t \"\"\"Handle OpenAI-compatible providers that need custom configuration.\"\"\"\r\n 167\t config = PROVIDER_CONFIGS[provider]\r\n 168\t provider_kwargs = provider_kwargs or {}\r\n 169\t \r\n 170\t # Build OpenAI provider kwargs\r\n 171\t openai_kwargs = {}\r\n 172\t \r\n 173\t # Set base URL\r\n 174\t if \"base_url\" in config:\r\n 175\t openai_kwargs[\"base_url\"] = provider_kwargs.get(\"base_url\", config[\"base_url\"])\r\n 176\t \r\n 177\t # Handle API key\r\n 178\t if \"api_key\" in config:\r\n 179\t openai_kwargs[\"api_key\"] = config[\"api_key\"]\r\n 180\t elif \"env_var\" in config:\r\n 181\t api_key = provider_kwargs.get(\"api_key\") or os.getenv(config[\"env_var\"])\r\n 182\t if api_key:\r\n 183\t openai_kwargs[\"api_key\"] = api_key\r\n 184\t \r\n 185\t # Create OpenAI provider\r\n 186\t provider_instance = OpenAIProvider(**openai_kwargs)\r\n 187\t \r\n 188\t # Create model with profile if specified\r\n 189\t if config.get(\"profile\"):\r\n 190\t profile = OpenAIModelProfile(**config[\"profile\"])\r\n 191\t return OpenAIResponsesModel(\r\n 192\t model_name,\r\n 193\t provider=provider_instance,\r\n 194\t profile=profile,\r\n 195\t )\r\n 196\t else:\r\n 197\t return OpenAIResponsesModel(\r\n 198\t model_name,\r\n 199\t provider=provider_instance,\r\n 200\t )\r\n 201\t\r\n 202\t\r\n 203\tdef handle_openrouter_model(\r\n 204\t model_name: str,\r\n 205\t provider_kwargs: Optional[Dict[str, Any]] = None,\r\n 206\t) -> Model:\r\n 207\t \"\"\"Handle OpenRouter models via an explicit provider/model.\r\n 208\t\r\n 209\t PydanticAI can accept `openrouter:` strings directly, but we build\r\n 210\t the provider/model explicitly so this repository owns configuration and can\r\n 211\t support optional app attribution.\r\n 212\t \"\"\"\r\n 213\t\r\n 214\t # Import lazily so users without the optional dependency can still import\r\n 215\t # the rest of the repo.\r\n 216\t #\r\n 217\t # NOTE: In pydantic-ai-slim==1.17.x, OpenRouter uses the OpenAI-compatible\r\n 218\t # models (Chat Completions) with an `OpenRouterProvider`, rather than a\r\n 219\t # dedicated `OpenRouterModel` class.\r\n 220\t import json\r\n 221\t\r\n 222\t from openai import AsyncOpenAI\r\n 223\t from openai.types import chat\r\n 224\t from pydantic import ValidationError\r\n 225\t from pydantic_ai.models.openai import OpenAIChatModel\r\n 226\t from pydantic_ai.providers.openrouter import OpenRouterProvider\r\n 227\t\r\n 228\t class OpenRouterPatchedChatModel(OpenAIChatModel):\r\n 229\t \"\"\"OpenAIChatModel with OpenRouter response sanitation.\r\n 230\t\r\n 231\t Some OpenRouter-backed models (notably Anthropic) occasionally return\r\n 232\t invalid tool-call payloads such as `function.arguments = null`. PydanticAI\r\n 233\t validates the OpenAI response strictly, which raises\r\n 234\t `UnexpectedModelBehavior` before tool execution can happen.\r\n 235\t\r\n 236\t We patch the raw ChatCompletion dict prior to Pydantic validation.\r\n 237\t \"\"\"\r\n 238\t\r\n 239\t def _process_response(self, response: chat.ChatCompletion | str): # type: ignore[override]\r\n 240\t if isinstance(response, chat.ChatCompletion):\r\n",{"_670":1498,"_662":663,"_672":81},{"_731":1499,"_733":1500,"_735":1501,"_737":738,"_739":801,"_741":31,"_41":1494,"_742":1502,"_744":745,"_667":111,"_90":746}," 1\t\"\"\"\r\n 2\tModel Factory for PydanticAI\r\n 3\t\r\n 4\tThis module provides a centralized way to create models across different providers,\r\n 5\tleveraging PydanticAI's built-in provider support and handling special cases.\r\n 6\t\"\"\"\r\n 7\t\r\n 8\timport os\r\n 9\timport re\r\n 10\tfrom typing import Any, Dict, Optional, Union\r\n 11\tfrom pydantic_ai import Agent, InlineDefsJsonSchemaTransformer\r\n 12\tfrom pydantic_ai.models import Model\r\n 13\tfrom pydantic_ai.models.openai import OpenAIResponsesModel\r\n 14\tfrom pydantic_ai.profiles.openai import OpenAIModelProfile\r\n 15\tfrom pydantic_ai.providers.openai import OpenAIProvider\r\n 16\t\r\n 17\t\r\n 18\t# Providers that PydanticAI handles natively with provider:model syntax.\r\n 19\t#\r\n 20\t# NOTE:\r\n 21\t# - `openrouter:*` is intentionally **not** treated as native in this repo.\r\n 22\t# We construct an explicit OpenRouter provider + OpenAI-compatible model so we\r\n 23\t# can control configuration and avoid relying on implicit provider detection.\r\n 24\tNATIVE_PROVIDERS = {\r\n 25\t \"deepseek\", \"github\", \"grok\", \"azure\",\r\n 26\t \"fireworks\", \"together\", \"heroku\"\r\n 27\t}\r\n 28\t\r\n 29\t# Special cases that need custom handling\r\n 30\tPROVIDER_CONFIGS = {\r\n 31\t \"bedrock\": {\r\n 32\t # Bedrock uses a different model class\r\n 33\t \"handler\": \"bedrock_handler\",\r\n 34\t \"env_vars\": {\r\n 35\t \"region\": \"AWS_REGION\",\r\n 36\t \"profile\": \"AWS_PROFILE\"\r\n 37\t }\r\n 38\t },\r\n 39\t \"ollama\": {\r\n 40\t # Ollama is OpenAI-compatible but needs custom base URL\r\n 41\t \"handler\": \"openai_compatible_handler\",\r\n 42\t \"base_url\": \"http://localhost:11434/v1\",\r\n 43\t \"api_key\": \"not-needed\",\r\n 44\t \"profile\": {\r\n 45\t \"json_schema_transformer\": InlineDefsJsonSchemaTransformer,\r\n 46\t \"openai_supports_strict_tool_definition\": False\r\n 47\t }\r\n 48\t },\r\n 49\t \"perplexity\": {\r\n 50\t # Perplexity is OpenAI-compatible with custom base URL\r\n 51\t \"handler\": \"openai_compatible_handler\",\r\n 52\t \"base_url\": \"https://api.perplexity.ai\",\r\n 53\t \"env_var\": \"PERPLEXITY_API_KEY\"\r\n 54\t },\r\n 55\t \"openrouter\": {\r\n 56\t # OpenRouter has first-class support in PydanticAI, but we construct the\r\n 57\t # provider/model explicitly so configuration is controlled by this repo.\r\n 58\t \"handler\": \"openrouter_handler\",\r\n 59\t \"env_var\": \"OPENROUTER_API_KEY\",\r\n 60\t \"env_vars\": {\r\n 61\t \"app_url\": \"OPENROUTER_APP_URL\",\r\n 62\t \"app_title\": \"OPENROUTER_APP_TITLE\",\r\n 63\t },\r\n 64\t }\r\n 65\t}\r\n 66\t\r\n 67\t\r\n 68\tdef ensure_openai_responses_model(model_string: str) -> str:\r\n 69\t \"\"\"Ensure OpenAI models use the Responses API.\r\n 70\t\r\n 71\t This helper normalises any OpenAI model identifier to the\r\n 72\t `openai-responses:` provider namespace so that PydanticAI\r\n 73\t will use the OpenAI Responses API rather than Chat Completions.\r\n 74\t\r\n 75\t Examples:\r\n 76\t \"gpt-5\" -> \"openai-responses:gpt-5\"\r\n 77\t \"openai:gpt-5-mini\" -> \"openai-responses:gpt-5-mini\"\r\n 78\t \"openai-responses:gpt-5\" -> \"openai-responses:gpt-5\" (unchanged)\r\n 79\t \"\"\"\r\n 80\t # Avoid double-prefixing if already using Responses API directly\r\n 81\t if model_string.startswith(\"openai-responses:\"):\r\n 82\t return model_string\r\n 83\t\r\n 84\t provider, model_name = parse_model_string(model_string)\r\n 85\t if provider == \"openai\":\r\n 86\t return f\"openai-responses:{model_name}\"\r\n 87\t\r\n 88\t return model_string\r\n 89\t\r\n 90\t\r\n 91\t# Accept optional reasoning hints for models that support it\r\n 92\tREASONING_SUFFIX_RE = re.compile(r\"\\s*\\((low|medium|high|none|minimal|xhigh)\\)\\s*$\", re.IGNORECASE)\r\n 93\t\r\n 94\t\r\n 95\tdef extract_reasoning_effort(model_string: str) -> tuple[str, Optional[str]]:\r\n 96\t \"\"\"Strip trailing reasoning hint like \"(medium)\" or \"(none)\" from model string.\r\n 97\t\r\n 98\t Returns the base model string and the effort level (lowercased) if present.\r\n 99\t \"\"\"\r\n 100\t\r\n 101\t match = REASONING_SUFFIX_RE.search(model_string)\r\n 102\t if not match:\r\n 103\t return model_string.strip(), None\r\n 104\t\r\n 105\t effort = match.group(1).lower()\r\n 106\t base = REASONING_SUFFIX_RE.sub(\"\", model_string).strip()\r\n 107\t return base, effort\r\n 108\t\r\n 109\t\r\n 110\tdef parse_model_string(model_string: str) -> tuple[Optional[str], str]:\r\n 111\t \"\"\"\r\n 112\t Parse a model string to extract provider and model name.\r\n 113\t \r\n 114\t Args:\r\n 115\t model_string: Model identifier (e.g., \"openai:gpt-4\", \"deepseek:deepseek-chat\", \"gemini-2.5-pro\")\r\n 116\t \r\n 117\t Returns:\r\n 118\t Tuple of (provider, model_name)\r\n 119\t \"\"\"\r\n 120\t if \":\" in model_string:\r\n 121\t provider, model_name = model_string.split(\":\", 1)\r\n 122\t return provider, model_name\r\n 123\t \r\n 124\t # Check for known prefixes without colon\r\n 125\t if model_string.startswith(\"gemini-\"):\r\n 126\t return \"google\", model_string\r\n 127\t elif model_string.startswith(\"gpt-\") or model_string.startswith(\"o1-\") or model_string.startswith(\"o3-\") or model_string.startswith(\"o4-\"):\r\n 128\t return \"openai\", model_string\r\n 129\t elif model_string.startswith(\"claude-\"):\r\n 130\t return \"anthropic\", model_string\r\n 131\t \r\n 132\t # Default to no specific provider\r\n 133\t return None, model_string\r\n 134\t\r\n 135\t\r\n 136\tdef handle_bedrock_model(\r\n 137\t model_name: str,\r\n 138\t provider_kwargs: Optional[Dict[str, Any]] = None\r\n 139\t) -> Model:\r\n 140\t \"\"\"Handle AWS Bedrock models which require special treatment.\"\"\"\r\n 141\t from pydantic_ai.models.bedrock import BedrockConverseModel\r\n 142\t from pydantic_ai.providers.bedrock import BedrockProvider\r\n 143\t \r\n 144\t config = PROVIDER_CONFIGS[\"bedrock\"]\r\n 145\t provider_kwargs = provider_kwargs or {}\r\n 146\t \r\n 147\t region = provider_kwargs.get(\"region_name\") or os.getenv(config[\"env_vars\"][\"region\"], \"us-east-1\")\r\n 148\t profile = provider_kwargs.get(\"profile_name\") or os.getenv(config[\"env_vars\"][\"profile\"], \"default\")\r\n 149\t \r\n 150\t provider_instance = BedrockProvider(\r\n 151\t region_name=region,\r\n 152\t profile_name=profile\r\n 153\t )\r\n 154\t \r\n 155\t return BedrockConverseModel(\r\n 156\t model_name,\r\n 157\t provider=provider_instance\r\n 158\t )\r\n 159\t\r\n 160\t\r\n 161\tdef handle_openai_compatible(\r\n 162\t provider: str,\r\n 163\t model_name: str,\r\n 164\t provider_kwargs: Optional[Dict[str, Any]] = None\r\n 165\t) -> Model:\r\n 166\t \"\"\"Handle OpenAI-compatible providers that need custom configuration.\"\"\"\r\n 167\t config = PROVIDER_CONFIGS[provider]\r\n 168\t provider_kwargs = provider_kwargs or {}\r\n 169\t \r\n 170\t # Build OpenAI provider kwargs\r\n 171\t openai_kwargs = {}\r\n 172\t \r\n 173\t # Set base URL\r\n 174\t if \"base_url\" in config:\r\n 175\t openai_kwargs[\"base_url\"] = provider_kwargs.get(\"base_url\", config[\"base_url\"])\r\n 176\t \r\n 177\t # Handle API key\r\n 178\t if \"api_key\" in config:\r\n 179\t openai_kwargs[\"api_key\"] = config[\"api_key\"]\r\n 180\t elif \"env_var\" in config:\r\n 181\t api_key = provider_kwargs.get(\"api_key\") or os.getenv(config[\"env_var\"])\r\n 182\t if api_key:\r\n 183\t openai_kwargs[\"api_key\"] = api_key\r\n 184\t \r\n 185\t # Create OpenAI provider\r\n 186\t provider_instance = OpenAIProvider(**openai_kwargs)\r\n 187\t \r\n 188\t # Create model with profile if specified\r\n 189\t if config.get(\"profile\"):\r\n 190\t profile = OpenAIModelProfile(**config[\"profile\"])\r\n 191\t return OpenAIResponsesModel(\r\n 192\t model_name,\r\n 193\t provider=provider_instance,\r\n 194\t profile=profile,\r\n 195\t )\r\n 196\t else:\r\n 197\t return OpenAIResponsesModel(\r\n 198\t model_name,\r\n 199\t provider=provider_instance,\r\n 200\t )\r\n 201\t\r\n 202\t\r\n 203\tdef handle_openrouter_model(\r\n 204\t model_name: str,\r\n 205\t provider_kwargs: Optional[Dict[str, Any]] = None,\r\n 206\t) -> Model:\r\n 207\t \"\"\"Handle OpenRouter models via an explicit provider/model.\r\n 208\t\r\n 209\t PydanticAI can accept `openrouter:` strings directly, but we build\r\n 210\t the provider/model explicitly so this repository owns configuration and can\r\n 211\t support optional app attribution.\r\n 212\t \"\"\"\r\n 213\t\r\n 214\t # Import lazily so users without the optional dependency can still import\r\n 215\t # the rest of the repo.\r\n 216\t #\r\n 217\t # NOTE: In pydantic-ai-slim==1.17.x, OpenRouter uses the OpenAI-compatible\r\n 218\t # models (Chat Completions) with an `OpenRouterProvider`, rather than a\r\n 219\t # dedicated `OpenRouterModel` class.\r\n 220\t import json\r\n 221\t\r\n 222\t from openai import AsyncOpenAI\r\n 223\t from openai.types import chat\r\n 224\t from pydantic import ValidationError\r\n 225\t from pydantic_ai.models.openai import OpenAIChatModel\r\n 226\t from pydantic_ai.providers.openrouter import OpenRouterProvider\r\n 227\t\r\n 228\t class OpenRouterPatchedChatModel(OpenAIChatModel):\r\n 229\t \"\"\"OpenAIChatModel with OpenRouter response sanitation.\r\n 230\t\r\n 231\t Some OpenRouter-backed models (notably Anthropic) occasionally return\r\n 232\t invalid tool-call payloads such as `function.arguments = null`. PydanticAI\r\n 233\t validates the OpenAI response strictly, which raises\r\n 234\t `UnexpectedModelBehavior` before tool execution can happen.\r\n 235\t\r\n 236\t We patch the raw ChatCompletion dict prior to Pydantic validation.\r\n 237\t \"\"\"\r\n 238\t\r\n 239\t def _process_response(self, response: chat.ChatCompletion | str): # type: ignore[override]\r\n 240\t if isinstance(response, chat.ChatCompletion):\r\n","/bin/bash -lc 'nl -ba agents_mcp_usage/factory/model_factory.py | sed -n '\"'\"'1,240p'\"'\"''",[1503],"17807",{"_733":1504,"_297":1467,"_216":1468,"_90":751},"nl -ba agents_mcp_usage/factory/model_factory.py | sed -n '1,240p'",{"_670":1506,"_662":663,"_672":81},{"_731":-5,"_733":1500,"_735":1507,"_737":738,"_739":-5,"_741":-5,"_41":1494,"_742":1502,"_744":745,"_667":755,"_90":746},[1508],{"_733":1504,"_297":1467,"_216":1468,"_90":751},{"_670":1510,"_662":663,"_672":81},{"_759":1511,"_722":1494,"_41":-5,"_297":761,"_762":-5,"_90":763},"{\"cmd\":\"nl -ba agents_mcp_usage/factory/model_factory.py | sed -n '1,240p'\"}",{"_670":1513,"_662":663,"_672":81},{"_673":-5,"_691":1514,"_41":1515,"_694":1516,"_90":696},"gAAAAABpaAix4vtvYnKBdTpVq9ac67kB1ZzJl7kVTXX1v7W9FQ4CAzCP4SiXW1CQGSduzcrevB6N48XcqJi38PDoRgxfdIRQ55o1-72rDraK-u7BYzFI_0QEUDMybEVRAywQieGzBrV8JpmMZVAFIk_Vj2Zs0ydSDiei2qraONldthdlRBVUbBM4cgMTza5pihhVCgj3n_vrM4E4Yaom0YYpAnXGNocdoSJwlP9xqlSI5tkdnDTqFrFAaMIsO6lYsVdxpuXswJH4y0h8EICBGd_DJ1w6mk8DFHC8CLs8KKHMSr5Iym3Nq1D6Pg3XQuuV2G3o99Rhi6my1aruoCNjgSEUrjwVblbRwg_1hdlZLwB7bYRtY84kWe6c_kOeqNk-wePyM8GJpRvrGfAkcx_Qw7BOAhn0JU-9_3xhOm3S47DjuQKHV1DCxVzJ4gGLPL-weHz49YWAhog8CWPahWH9BRd1rcPtb25L0iBHZ5qILzjGtiEEw39ZQZ71O6_jfq98Ji5kz8ySzk3zeBKvfbqCaturOUFiCwUOwlUKOBnxkfHEeMcl0E1yKLHsVfVLfflNcwI-NS5lbq1jhVznp8XmNsHYEkZxkYPOpJX0SmJH_aaapcf5jKcdPiWinBZT_TtG-B_2cnRlFnua7zTWF30L7HJ6_UbeQaYKFmO2ozN9RT5F7mHW5rH8lLfuyUT62qbDhMSp5y0brWfpixJx_RQxNMcByQZJMKxXJG0456bSh7GcfiDSRNMp4QVNkgmfuLpTiSlB0u1Hw_jAilbVto3j8EwPzMMVjp_M8DXgoGtWFehaxFIaacoN3VRFRxgr2-x8WY3HZJ9pH2BFqMjEyd_wUAddZAlpMp9lSV7TMdoDd6zymBtML7JVj7sLZTZH1pTDz7rDabBXqMalmKmq9UQhYMlLrZur0aKr-g==","legacy_responseItem_reasoning_21",[1517],{"_45":1518,"_90":700},"**Adding citations for model factory**\n\nI'm preparing to insert citations in the model factory documentation and code using natural language references.",{"_670":1520,"_662":663,"_672":81},{"_673":1521,"_41":1522,"_694":1523,"_90":696},[1518],"legacy_reasoning_20",[],{"_670":1525,"_662":663,"_672":81},{"_673":1526,"_41":1522,"_694":1527,"_90":696},[],[],{"_670":1529,"_662":663,"_672":81},{"_722":1530,"_724":1531,"_90":726},"call_W4geGjKQqRWssIpytF02hLOs",{"_408":1532,"_728":-5},"Chunk ID: c443d9\nWall time: 0.0515 seconds\nProcess exited with code 0\nOriginal token count: 2127\nOutput:\n 1\t\"\"\"\r\n 2\tPydantic MCP Example using Model Factory Pattern\r\n 3\t\r\n 4\tThis example demonstrates how to use the model factory to create agents\r\n 5\twith different providers while maintaining the same MCP server setup.\r\n 6\t\r\n 7\tSupported model formats:\r\n 8\t- Standard: \"gemini-2.5-pro\", \"gpt-4\", \"claude-3-opus\"\r\n 9\t- Provider-prefixed: \"openai:gpt-4\", \"deepseek:deepseek-chat\", \"bedrock:us.amazon.nova-pro-v1:0\"\r\n 10\t- Local models: \"ollama:llama3.2\", \"ollama:qwen2.5-coder:7b\"\r\n 11\t\"\"\"\r\n 12\t\r\n 13\timport asyncio\r\n 14\timport argparse\r\n 15\tfrom typing import Optional\r\n 16\t\r\n 17\timport logfire\r\n 18\tfrom dotenv import load_dotenv\r\n 19\tfrom pydantic_ai.mcp import MCPServerStdio\r\n 20\t\r\n 21\tfrom agents_mcp_usage.utils import get_mcp_server_path\r\n 22\tfrom agents_mcp_usage.factory.model_factory import create_agent\r\n 23\t\r\n 24\tload_dotenv()\r\n 25\t\r\n 26\t# Configure logging to logfire if LOGFIRE_TOKEN is set in environment\r\n 27\tlogfire.configure(send_to_logfire=\"if-token-present\", service_name=\"pydantic-basic-mcp-factory\")\r\n 28\tlogfire.instrument_mcp()\r\n 29\tlogfire.instrument_pydantic_ai()\r\n 30\t\r\n 31\t# Preset model aliases demonstrating different provider formats\r\n 32\tMODEL_ALIASES = {\r\n 33\t # Standard providers (auto-detected)\r\n 34\t \"gemini\": \"gemini-2.5-pro-preview-06-05\",\r\n 35\t \"gemini-flash\": \"gemini-2.5-flash\",\r\n 36\t \"gpt4\": \"gpt-4\",\r\n 37\t \"claude\": \"claude-3-opus-20240229\",\r\n 38\t \r\n 39\t # Explicit provider prefix\r\n 40\t \"openai-o4\": \"openai:o4-mini\",\r\n 41\t \"deepseek\": \"deepseek:deepseek-chat\",\r\n 42\t \"deepseek-reasoner\": \"deepseek:deepseek-reasoner\",\r\n 43\t \r\n 44\t # AWS Bedrock models\r\n 45\t \"bedrock-nova\": \"bedrock:us.amazon.nova-pro-v1:0\",\r\n 46\t \"bedrock-claude\": \"bedrock:us.anthropic.claude-3-5-sonnet-20240620-v1:0\",\r\n 47\t \r\n 48\t # GitHub Models (free tier)\r\n 49\t \"github-grok\": \"github:xai/grok-3-mini\",\r\n 50\t \r\n 51\t # Local models via Ollama\r\n 52\t \"ollama-llama\": \"ollama:llama3.2\",\r\n 53\t \"ollama-qwen\": \"ollama:qwen2.5-coder:7b\",\r\n 54\t \r\n 55\t # Other providers\r\n 56\t \"perplexity\": \"perplexity:sonar-pro\",\r\n 57\t \"grok\": \"grok:grok-2-1212\",\r\n 58\t}\r\n 59\t\r\n 60\t\r\n 61\tdef get_mcp_server() -> MCPServerStdio:\r\n 62\t \"\"\"Get the MCP server configuration.\"\"\"\r\n 63\t return MCPServerStdio(\r\n 64\t command=\"uv\",\r\n 65\t args=[\r\n 66\t \"run\",\r\n 67\t str(get_mcp_server_path(\"example_server.py\")),\r\n 68\t \"stdio\",\r\n 69\t ],\r\n 70\t )\r\n 71\t\r\n 72\t\r\n 73\tasync def main(\r\n 74\t model: str = \"gemini-2.5-pro-preview-06-05\",\r\n 75\t query: str = \"Greet Andrew and give him the current time\",\r\n 76\t provider_kwargs: Optional[dict] = None\r\n 77\t) -> None:\r\n 78\t \"\"\"Runs the Pydantic agent with a given query using the model factory.\r\n 79\t\r\n 80\t This function demonstrates how to use the model factory to create agents\r\n 81\t with different providers while maintaining the same MCP functionality.\r\n 82\t\r\n 83\t Args:\r\n 84\t model: The model identifier (e.g., \"gemini-2.5-pro\", \"deepseek:deepseek-chat\")\r\n 85\t query: The query to run the agent with\r\n 86\t provider_kwargs: Optional provider-specific configuration (e.g., for Azure)\r\n 87\t \"\"\"\r\n 88\t # Create MCP server\r\n 89\t server = get_mcp_server()\r\n 90\t \r\n 91\t # Create agent using the factory\r\n 92\t print(f\"Creating agent with model: {model}\")\r\n 93\t agent = create_agent(\r\n 94\t model=model,\r\n 95\t mcp_servers=[server],\r\n 96\t provider_kwargs=provider_kwargs\r\n 97\t )\r\n 98\t \r\n 99\t # Run the agent\r\n 100\t try:\r\n 101\t async with agent.run_mcp_servers():\r\n 102\t result = await agent.run(query)\r\n 103\t print(f\"\\nResult from {model}:\")\r\n 104\t print(result.output)\r\n 105\t \r\n 106\t # Print usage information if available\r\n 107\t usage = result.usage()\r\n 108\t if usage:\r\n 109\t print(f\"\\nToken usage: {usage.total_tokens} total \"\r\n 110\t f\"({usage.request_tokens} in, {usage.response_tokens} out)\")\r\n 111\t \r\n 112\t except Exception as e:\r\n 113\t print(f\"Error running agent: {e}\")\r\n 114\t print(\"\\nMake sure you have the required API keys set:\")\r\n 115\t print(\"- GEMINI_API_KEY for Gemini models\")\r\n 116\t print(\"- OPENAI_API_KEY for OpenAI models\")\r\n 117\t print(\"- DEEPSEEK_API_KEY for DeepSeek models\")\r\n 118\t print(\"- AWS credentials for Bedrock models\")\r\n 119\t print(\"- Other provider-specific keys as needed\")\r\n 120\t\r\n 121\t\r\n 122\tdef parse_arguments():\r\n 123\t \"\"\"Parse command-line arguments.\"\"\"\r\n 124\t parser = argparse.ArgumentParser(\r\n 125\t description=\"Run Pydantic MCP agent with different model providers\",\r\n 126\t formatter_class=argparse.RawDescriptionHelpFormatter,\r\n 127\t epilog=\"\"\"\r\n 128\tExamples:\r\n 129\t # Use default Gemini model\r\n 130\t python pydantic_mcp_factory.py\r\n 131\t \r\n 132\t # Use a specific model by alias\r\n 133\t python pydantic_mcp_factory.py --model deepseek\r\n 134\t \r\n 135\t # Use a model by full name\r\n 136\t python pydantic_mcp_factory.py --model \"openai:gpt-4\"\r\n 137\t \r\n 138\t # Use a custom query\r\n 139\t python pydantic_mcp_factory.py --query \"What's the weather like?\"\r\n 140\t \r\n 141\t # List available model aliases\r\n 142\t python pydantic_mcp_factory.py --list-models\r\n 143\t \"\"\"\r\n 144\t )\r\n 145\t \r\n 146\t parser.add_argument(\r\n 147\t \"--model\", \"-m\",\r\n 148\t default=\"gemini\",\r\n 149\t help=\"Model to use (alias or full model string)\"\r\n 150\t )\r\n 151\t \r\n 152\t parser.add_argument(\r\n 153\t \"--query\", \"-q\",\r\n 154\t default=\"Greet Andrew and give him the current time\",\r\n 155\t help=\"Query to send to the agent\"\r\n 156\t )\r\n 157\t \r\n 158\t parser.add_argument(\r\n 159\t \"--list-models\", \"-l\",\r\n 160\t action=\"store_true\",\r\n 161\t help=\"List available model aliases and exit\"\r\n 162\t )\r\n 163\t \r\n 164\t # Provider-specific options\r\n 165\t parser.add_argument(\r\n 166\t \"--azure-endpoint\",\r\n 167\t help=\"Azure endpoint for Azure AI Foundry models\"\r\n 168\t )\r\n 169\t \r\n 170\t parser.add_argument(\r\n 171\t \"--azure-api-version\",\r\n 172\t help=\"Azure API version for Azure AI Foundry models\"\r\n 173\t )\r\n 174\t \r\n 175\t parser.add_argument(\r\n 176\t \"--ollama-base-url\",\r\n 177\t default=\"http://localhost:11434/v1\",\r\n 178\t help=\"Base URL for Ollama server\"\r\n 179\t )\r\n 180\t \r\n 181\t return parser.parse_args()\r\n 182\t\r\n 183\t\r\n 184\tif __name__ == \"__main__\":\r\n 185\t args = parse_arguments()\r\n 186\t \r\n 187\t # List models and exit if requested\r\n 188\t if args.list_models:\r\n 189\t print(\"Available model aliases:\")\r\n 190\t print(\"-\" * 50)\r\n 191\t for alias, model_string in MODEL_ALIASES.items():\r\n 192\t print(f\"{alias:<20} -> {model_string}\")\r\n 193\t print(\"\\nYou can also use any model string directly, e.g.:\")\r\n 194\t print(\" openrouter:google/gemini-2.5-pro-preview\")\r\n 195\t print(\" fireworks:accounts/fireworks/models/qwq-32b\")\r\n 196\t print(\" together:meta-llama/Llama-3.3-70B-Instruct-Turbo-Free\")\r\n 197\t exit(0)\r\n 198\t \r\n 199\t # Resolve model alias if applicable\r\n 200\t model = MODEL_ALIASES.get(args.model, args.model)\r\n 201\t \r\n 202\t # Build provider kwargs if Azure options are provided\r\n 203\t provider_kwargs = {}\r\n 204\t if args.azure_endpoint:\r\n 205\t provider_kwargs[\"azure_endpoint\"] = args.azure_endpoint\r\n 206\t if args.azure_api_version:\r\n 207\t provider_kwargs[\"api_version\"] = args.azure_api_version\r\n 208\t if args.ollama_base_url and model.startswith(\"ollama:\"):\r\n 209\t provider_kwargs[\"base_url\"] = args.ollama_base_url\r\n 210\t \r\n 211\t # Run the main function\r\n 212\t asyncio.run(main(\r\n 213\t model=model,\r\n 214\t query=args.query,\r\n 215\t provider_kwargs=provider_kwargs if provider_kwargs else None\r\n 216\t ))\r\n",{"_670":1534,"_662":663,"_672":81},{"_731":1535,"_733":1536,"_735":1537,"_737":738,"_739":740,"_741":31,"_41":1530,"_742":1538,"_744":745,"_667":111,"_90":746}," 1\t\"\"\"\r\n 2\tPydantic MCP Example using Model Factory Pattern\r\n 3\t\r\n 4\tThis example demonstrates how to use the model factory to create agents\r\n 5\twith different providers while maintaining the same MCP server setup.\r\n 6\t\r\n 7\tSupported model formats:\r\n 8\t- Standard: \"gemini-2.5-pro\", \"gpt-4\", \"claude-3-opus\"\r\n 9\t- Provider-prefixed: \"openai:gpt-4\", \"deepseek:deepseek-chat\", \"bedrock:us.amazon.nova-pro-v1:0\"\r\n 10\t- Local models: \"ollama:llama3.2\", \"ollama:qwen2.5-coder:7b\"\r\n 11\t\"\"\"\r\n 12\t\r\n 13\timport asyncio\r\n 14\timport argparse\r\n 15\tfrom typing import Optional\r\n 16\t\r\n 17\timport logfire\r\n 18\tfrom dotenv import load_dotenv\r\n 19\tfrom pydantic_ai.mcp import MCPServerStdio\r\n 20\t\r\n 21\tfrom agents_mcp_usage.utils import get_mcp_server_path\r\n 22\tfrom agents_mcp_usage.factory.model_factory import create_agent\r\n 23\t\r\n 24\tload_dotenv()\r\n 25\t\r\n 26\t# Configure logging to logfire if LOGFIRE_TOKEN is set in environment\r\n 27\tlogfire.configure(send_to_logfire=\"if-token-present\", service_name=\"pydantic-basic-mcp-factory\")\r\n 28\tlogfire.instrument_mcp()\r\n 29\tlogfire.instrument_pydantic_ai()\r\n 30\t\r\n 31\t# Preset model aliases demonstrating different provider formats\r\n 32\tMODEL_ALIASES = {\r\n 33\t # Standard providers (auto-detected)\r\n 34\t \"gemini\": \"gemini-2.5-pro-preview-06-05\",\r\n 35\t \"gemini-flash\": \"gemini-2.5-flash\",\r\n 36\t \"gpt4\": \"gpt-4\",\r\n 37\t \"claude\": \"claude-3-opus-20240229\",\r\n 38\t \r\n 39\t # Explicit provider prefix\r\n 40\t \"openai-o4\": \"openai:o4-mini\",\r\n 41\t \"deepseek\": \"deepseek:deepseek-chat\",\r\n 42\t \"deepseek-reasoner\": \"deepseek:deepseek-reasoner\",\r\n 43\t \r\n 44\t # AWS Bedrock models\r\n 45\t \"bedrock-nova\": \"bedrock:us.amazon.nova-pro-v1:0\",\r\n 46\t \"bedrock-claude\": \"bedrock:us.anthropic.claude-3-5-sonnet-20240620-v1:0\",\r\n 47\t \r\n 48\t # GitHub Models (free tier)\r\n 49\t \"github-grok\": \"github:xai/grok-3-mini\",\r\n 50\t \r\n 51\t # Local models via Ollama\r\n 52\t \"ollama-llama\": \"ollama:llama3.2\",\r\n 53\t \"ollama-qwen\": \"ollama:qwen2.5-coder:7b\",\r\n 54\t \r\n 55\t # Other providers\r\n 56\t \"perplexity\": \"perplexity:sonar-pro\",\r\n 57\t \"grok\": \"grok:grok-2-1212\",\r\n 58\t}\r\n 59\t\r\n 60\t\r\n 61\tdef get_mcp_server() -> MCPServerStdio:\r\n 62\t \"\"\"Get the MCP server configuration.\"\"\"\r\n 63\t return MCPServerStdio(\r\n 64\t command=\"uv\",\r\n 65\t args=[\r\n 66\t \"run\",\r\n 67\t str(get_mcp_server_path(\"example_server.py\")),\r\n 68\t \"stdio\",\r\n 69\t ],\r\n 70\t )\r\n 71\t\r\n 72\t\r\n 73\tasync def main(\r\n 74\t model: str = \"gemini-2.5-pro-preview-06-05\",\r\n 75\t query: str = \"Greet Andrew and give him the current time\",\r\n 76\t provider_kwargs: Optional[dict] = None\r\n 77\t) -> None:\r\n 78\t \"\"\"Runs the Pydantic agent with a given query using the model factory.\r\n 79\t\r\n 80\t This function demonstrates how to use the model factory to create agents\r\n 81\t with different providers while maintaining the same MCP functionality.\r\n 82\t\r\n 83\t Args:\r\n 84\t model: The model identifier (e.g., \"gemini-2.5-pro\", \"deepseek:deepseek-chat\")\r\n 85\t query: The query to run the agent with\r\n 86\t provider_kwargs: Optional provider-specific configuration (e.g., for Azure)\r\n 87\t \"\"\"\r\n 88\t # Create MCP server\r\n 89\t server = get_mcp_server()\r\n 90\t \r\n 91\t # Create agent using the factory\r\n 92\t print(f\"Creating agent with model: {model}\")\r\n 93\t agent = create_agent(\r\n 94\t model=model,\r\n 95\t mcp_servers=[server],\r\n 96\t provider_kwargs=provider_kwargs\r\n 97\t )\r\n 98\t \r\n 99\t # Run the agent\r\n 100\t try:\r\n 101\t async with agent.run_mcp_servers():\r\n 102\t result = await agent.run(query)\r\n 103\t print(f\"\\nResult from {model}:\")\r\n 104\t print(result.output)\r\n 105\t \r\n 106\t # Print usage information if available\r\n 107\t usage = result.usage()\r\n 108\t if usage:\r\n 109\t print(f\"\\nToken usage: {usage.total_tokens} total \"\r\n 110\t f\"({usage.request_tokens} in, {usage.response_tokens} out)\")\r\n 111\t \r\n 112\t except Exception as e:\r\n 113\t print(f\"Error running agent: {e}\")\r\n 114\t print(\"\\nMake sure you have the required API keys set:\")\r\n 115\t print(\"- GEMINI_API_KEY for Gemini models\")\r\n 116\t print(\"- OPENAI_API_KEY for OpenAI models\")\r\n 117\t print(\"- DEEPSEEK_API_KEY for DeepSeek models\")\r\n 118\t print(\"- AWS credentials for Bedrock models\")\r\n 119\t print(\"- Other provider-specific keys as needed\")\r\n 120\t\r\n 121\t\r\n 122\tdef parse_arguments():\r\n 123\t \"\"\"Parse command-line arguments.\"\"\"\r\n 124\t parser = argparse.ArgumentParser(\r\n 125\t description=\"Run Pydantic MCP agent with different model providers\",\r\n 126\t formatter_class=argparse.RawDescriptionHelpFormatter,\r\n 127\t epilog=\"\"\"\r\n 128\tExamples:\r\n 129\t # Use default Gemini model\r\n 130\t python pydantic_mcp_factory.py\r\n 131\t \r\n 132\t # Use a specific model by alias\r\n 133\t python pydantic_mcp_factory.py --model deepseek\r\n 134\t \r\n 135\t # Use a model by full name\r\n 136\t python pydantic_mcp_factory.py --model \"openai:gpt-4\"\r\n 137\t \r\n 138\t # Use a custom query\r\n 139\t python pydantic_mcp_factory.py --query \"What's the weather like?\"\r\n 140\t \r\n 141\t # List available model aliases\r\n 142\t python pydantic_mcp_factory.py --list-models\r\n 143\t \"\"\"\r\n 144\t )\r\n 145\t \r\n 146\t parser.add_argument(\r\n 147\t \"--model\", \"-m\",\r\n 148\t default=\"gemini\",\r\n 149\t help=\"Model to use (alias or full model string)\"\r\n 150\t )\r\n 151\t \r\n 152\t parser.add_argument(\r\n 153\t \"--query\", \"-q\",\r\n 154\t default=\"Greet Andrew and give him the current time\",\r\n 155\t help=\"Query to send to the agent\"\r\n 156\t )\r\n 157\t \r\n 158\t parser.add_argument(\r\n 159\t \"--list-models\", \"-l\",\r\n 160\t action=\"store_true\",\r\n 161\t help=\"List available model aliases and exit\"\r\n 162\t )\r\n 163\t \r\n 164\t # Provider-specific options\r\n 165\t parser.add_argument(\r\n 166\t \"--azure-endpoint\",\r\n 167\t help=\"Azure endpoint for Azure AI Foundry models\"\r\n 168\t )\r\n 169\t \r\n 170\t parser.add_argument(\r\n 171\t \"--azure-api-version\",\r\n 172\t help=\"Azure API version for Azure AI Foundry models\"\r\n 173\t )\r\n 174\t \r\n 175\t parser.add_argument(\r\n 176\t \"--ollama-base-url\",\r\n 177\t default=\"http://localhost:11434/v1\",\r\n 178\t help=\"Base URL for Ollama server\"\r\n 179\t )\r\n 180\t \r\n 181\t return parser.parse_args()\r\n 182\t\r\n 183\t\r\n 184\tif __name__ == \"__main__\":\r\n 185\t args = parse_arguments()\r\n 186\t \r\n 187\t # List models and exit if requested\r\n 188\t if args.list_models:\r\n 189\t print(\"Available model aliases:\")\r\n 190\t print(\"-\" * 50)\r\n 191\t for alias, model_string in MODEL_ALIASES.items():\r\n 192\t print(f\"{alias:<20} -> {model_string}\")\r\n 193\t print(\"\\nYou can also use any model string directly, e.g.:\")\r\n 194\t print(\" openrouter:google/gemini-2.5-pro-preview\")\r\n 195\t print(\" fireworks:accounts/fireworks/models/qwq-32b\")\r\n 196\t print(\" together:meta-llama/Llama-3.3-70B-Instruct-Turbo-Free\")\r\n 197\t exit(0)\r\n 198\t \r\n 199\t # Resolve model alias if applicable\r\n 200\t model = MODEL_ALIASES.get(args.model, args.model)\r\n 201\t \r\n 202\t # Build provider kwargs if Azure options are provided\r\n 203\t provider_kwargs = {}\r\n 204\t if args.azure_endpoint:\r\n 205\t provider_kwargs[\"azure_endpoint\"] = args.azure_endpoint\r\n 206\t if args.azure_api_version:\r\n 207\t provider_kwargs[\"api_version\"] = args.azure_api_version\r\n 208\t if args.ollama_base_url and model.startswith(\"ollama:\"):\r\n 209\t provider_kwargs[\"base_url\"] = args.ollama_base_url\r\n 210\t \r\n 211\t # Run the main function\r\n 212\t asyncio.run(main(\r\n 213\t model=model,\r\n 214\t query=args.query,\r\n 215\t provider_kwargs=provider_kwargs if provider_kwargs else None\r\n 216\t ))\r\n","/bin/bash -lc 'nl -ba agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py | sed -n '\"'\"'1,220p'\"'\"''",[1539],"50692",{"_733":1540,"_297":1541,"_216":1542,"_90":751},"nl -ba agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py | sed -n '1,220p'","pydantic_mcp_factory.py","agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py",{"_670":1544,"_662":663,"_672":81},{"_731":-5,"_733":1536,"_735":1545,"_737":738,"_739":-5,"_741":-5,"_41":1530,"_742":1538,"_744":745,"_667":755,"_90":746},[1546],{"_733":1540,"_297":1541,"_216":1542,"_90":751},{"_670":1548,"_662":663,"_672":81},{"_759":1549,"_722":1530,"_41":-5,"_297":761,"_762":-5,"_90":763},"{\"cmd\":\"nl -ba agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py | sed -n '1,220p'\"}",{"_670":1551,"_662":663,"_672":81},{"_673":-5,"_691":1552,"_41":1553,"_694":1554,"_90":696},"gAAAAABpaAivmCuyW7wpVOs_I2V7Fco5G8oeEpEnPdNGeKt-O7DI9aE_MlioJU0EaXlpve-HVV4-54V4aK_2chuR_Es6b7zJ--LVYfgyeIkDm6uFFpHW5oeJGVCb2IgNsCSHZ8H6zpCvGowvPlFWC_STpnG7DtIhHEfO76FT1ndh2Uwjh5OdP5OtHmu_T8NAJZ24oTg3QnOH4u7b7fdTfyESOIEH5JzKiMeXo2Fb_pIOxrw5W_9LXdycXCoKKN0OtoNJ1YtkLH3DvSCY7W5-iSr2b6y5qxt6AU2i2olmTK2PUDvLExjR9X_pM2izKdqZFFs3p_ITsnk2J28E021vvYAUvp7mcz781yyYN9vVXiZyRJN1IFmUsRrUDOnImgzUON3vbsZ4HVUlruoPSIgeQGPTsX6n7N0fBR4_z8KXfj3ChGfMjIbHQR4i9yGVQ_E_egpVy3ojInrGtn8ToSKJdBSQ6QO0ntFrmsG4bgLcayjxXuI74qLCGxoaD69Gp66k7Gt_l_I-csS3xXeAT-r58IwJCu0YRSGRiIs-OezaNoxIu8XzdE6O1vu4qCdUIch9Rsgxn0qtsMvrfcApYg1g6pMR3HFn_7A3z1avjSYjrDtzKoZKWsS-Ae9tyHUIR1j4s68qJZspBtBLTyP76MvFUq4vHPwQ6XsvRnP-1pVdg4pVT7hXlQ_2urcxmSkav3M5hDT9Qz9grrb5IpFgDiQIVCvHphqrRih3qg45o4NSTQCZNYNj_oHlPEDx4q1ywUg9HIDuyZCDh7RlaoR8bOeRtFAPR2bauNb1EwIY_WyDouG_4c8-_TU0a8TyZV5kH5EOPFsVmyO5oiNcjQCBQygyJzjmfZ19W2voh4Ga9zL-FoGqs9Gho85_peXZBRdtFYDCbpoQT_YGTHa8qWrObwClBR-mbTj2lY61oCcBF-oapdksErjXUuerlBnXMgk5Ac371X7sbcJlrPR6_RXe6xaNSTMKY0vIuVy5dZL76obh3jYYRxccdSMoznhS8Lb-SJRysull6ZBAn71yXj_qSOlrgBruGqxXzUAPMMIfJWfN5cLiop3Kt7Muz43-bJratJLBZkbIRkdcm67ItqisajR0-GtOfj8y0KIXmcrX5HJ1iP4eLoQPSZDuEDnsnrcV3OlLn7-1MshM06HdHxpab6LkS_xSiK9r00NJUcf8eJ4yhzAqhseg-egxmTq_vU9pMu3BLwLPumBOFoLKD-xnV7Ux-uefZJYFTneZv4IIpMJcEF8IuJuviZQ3vllSveS6nshAWrrGshR9hgPgO9QfTdrngVasXoZ_IJFMXUiS11hParCqptIPytb1Zdilki7FmsuKyBiVFimOC8ve1yXk5XDyb7iHa8g-oovIMG5m7sszKUxwXSgQvk03nn45OqEOi-j5iBUb-lwTLFryJdaB8LA993dz02J3LHdaUq7wZTTsEQZSLbf9e5gAKxn6-PtyvejcVxhn9mHnl9qxKQ2V7kI_gLCJmZjRNoSu2XxfEFTVULe5Xqbe0AKggh318wxXb0dob2uSdb3A10sn6Tf0FN9xvPlVqPr_uliznWFFutNvxpL681v8SUtwy8h7FOl8IqIM5kvrooQjHqdhQDO4Ux4qXq7TdQT6G5KpDniXKlO8CyMpoBeSZYp2STCEsBjoHjfcjCdpNeV5oJ7fbrHzGotGFO440_1u5pajo4ifQ8HtPxW7qZ9VOlDxMqNgZltPAoSiuYTAIW9L64C8QmWdh4berQtHtUE3gby_OQ8frrJ5iRkvyc4RCotKzre80p5My9yQpID9VTuPxMPL4FP00NHSdYNrM_rI4nXP0CeLVK82SdWl1hP2vHVahN8YGYxvHkdSOh6jJX_KxFqu1gi0UTaeqx3NFchSJuTbtrsE9jw3VZJav-cIVqd24_WF27OMmrued_5f010ZJz_2M81y51h1WQVlJuMvdnlgMUFVhjoB_aTtSeExbZHwZw--DZoYWWdjAx7GMAXK-pDtF6bmQeZ2z0Dk_S94WLBcc1TgA5LdXrDv3fHujYMYrr8HI2D4jiyEfBl6pH50WhuP4rpj-qnQx2VOAoZ8aqvTW6ThuIhsyef-M0WHX2cG4WCCXWQ_QIPHeKAH0qMJgJJAb86bRmq0C_EUfgH9LEN2PQ4iIkAoVUv11KKZNC8fYXAekmGwd-1ALyF-ViJbMcR4H94U30TgpeuzQVqVC3bMrHhFwnDyGVlXCDuvChhpe53HCtWDNKX91MoK9D-yr_rvSExBct7ghctBhLucHVRuqbMLCzX-jzCnvEuN7t1A4bESdxGP3rqaks6uNXJ9CJg886vpvC-hNvhfNJ3CKvb_SKJUIwfnSilxt7nxNrVDEBaxL1pyrfauxFBcOquT2qhNiDYutNBrbdAatVLnFdAGyxuh-ABePGOViiC3PPqSd6UYDeazWl5hDFroiQ55pvu7tl1_aOpbJM5y6wcs9F8nuOwPbJrnYbwXQtg-ZB1_XZ_kdCc8T6ZnHli36IUnlvUd5iu48hgzF0a1JoA3Bf_tpPZ7iZc2esLDR2WjwUk0klyMbuAwLk7Js2MxbfrqLR_XuFfKP6iCxDGCQLpThcKGlFw-b38xZQwSGZH01hAC7s7Yza91INWdaBBkXasyXqG75bj0IBq6cEcuMAQhJXL7J-uETn8p0cbA8T0efirnWjnVzvTz0x4XCTtHWuHKwCLaflpBCxxxzNVbWvvd7hNpq7fiXkxC2ISpcTmAwBfT1sF3DJjth9DQfGXB7v9XN8LyqgznNNmiswEINxPTsGqQTYDqmFL32OnsHix7CqoysOA3hFtGsm-xutclojKKkvfiH7NrQiHinW44nHrMRk8IA7tmIwyFbLDsldXFhzVQ3Y0NWHOv13ZJBO4hZogkjDq8jHrRzSa6LQEuWfSmKKUzAZsa5_aWIhDicweybhfRuTZTyvKcP2bjdscsSg2LpMjxz8vcbpOy2GP2B38DAz3HSNC7pFyMPpl8zv0kHPrmVoe4Lr5gjTAiIL4bqLKig27HsyfM2cUYAi15vxkRhP9wnduY8IN9yGSQZN6bfaHsXSU=","legacy_responseItem_reasoning_19",[1555,1556],{"_45":1558,"_90":700},{"_45":1557,"_90":700},"**Planning line number extraction**\n\nI'm preparing to extract precise line numbers from key new and modified files using nl -ba to accurately cite changes in the PR description, focusing on main feature files like the model factory and mermaid evals.","**Compiling PR description plan**\n\nI'm preparing to generate a detailed PR description that covers all new features and improvements, including the model factory module, openrouter support, mermaid evaluation enhancements, cost updates with preprocessing, new merge and verify scripts, and the pinned mermaid validator CLI version. The PR description will include sections for Summary, Why, How to test/verify, and Impact, with citations referencing specific files and line numbers to clearly mark changes.",{"_670":1560,"_662":663,"_672":81},{"_673":1561,"_41":1562,"_694":1563,"_90":696},[1557],"legacy_reasoning_18",[],{"_670":1565,"_662":663,"_672":81},{"_673":1566,"_41":1562,"_694":1567,"_90":696},[],[],{"_670":1569,"_662":663,"_672":81},{"_673":1570,"_41":1571,"_694":1572,"_90":696},[1558],"legacy_reasoning_17",[],{"_670":1574,"_662":663,"_672":81},{"_673":1575,"_41":1571,"_694":1576,"_90":696},[],[],{"_670":1578,"_662":663,"_672":81},{"_722":1579,"_724":1580,"_90":726},"call_8wEDhNnJcHUh8CBu410x6MVB",{"_408":1581,"_728":-5},"Chunk ID: 7ec8b1\nWall time: 0.0514 seconds\nProcess exited with code 0\nOriginal token count: 66\nOutput:\ndiff --git a/.env.example b/.env.example\r\nindex 46f9522..b2424fd 100644\r\n--- a/.env.example\r\n+++ b/.env.example\r\n@@ -1,5 +1,6 @@\r\n GEMINI_API_KEY=\r\n OPENAI_API_KEY=\r\n+OPENROUTER_API_KEY=\r\n LOGFIRE_TOKEN=\r\n AWS_REGION=\r\n AWS_PROFILE=\r\n\\ No newline at end of file\r\n",{"_670":1583,"_662":663,"_672":81},{"_731":1584,"_733":1585,"_735":1586,"_737":738,"_739":740,"_741":31,"_41":1579,"_742":1587,"_744":745,"_667":111,"_90":746},"diff --git a/.env.example b/.env.example\r\nindex 46f9522..b2424fd 100644\r\n--- a/.env.example\r\n+++ b/.env.example\r\n@@ -1,5 +1,6 @@\r\n GEMINI_API_KEY=\r\n OPENAI_API_KEY=\r\n+OPENROUTER_API_KEY=\r\n LOGFIRE_TOKEN=\r\n AWS_REGION=\r\n AWS_PROFILE=\r\n\\ No newline at end of file\r\n","/bin/bash -lc 'git diff 697c3fc..HEAD -- .env.example'",[1588],"69638",{"_733":1589,"_90":1590},"git diff 697c3fc..HEAD -- .env.example","unknown",{"_670":1592,"_662":663,"_672":81},{"_731":-5,"_733":1585,"_735":1593,"_737":738,"_739":-5,"_741":-5,"_41":1579,"_742":1587,"_744":745,"_667":755,"_90":746},[1594],{"_733":1589,"_90":1590},{"_670":1596,"_662":663,"_672":81},{"_759":1597,"_722":1579,"_41":-5,"_297":761,"_762":-5,"_90":763},"{\"cmd\":\"git diff 697c3fc..HEAD -- .env.example\"}",{"_670":1599,"_662":663,"_672":81},{"_673":-5,"_691":1600,"_41":1601,"_694":1602,"_90":696},"gAAAAABpaAikqjFEHbHgVaTbdGzUERV432y1dvAo-bfHAc4QlQRBxFYF2feiRhPTZJ3rRDmh-rwtFfl1yDdAv4VJUcNDmK9b20i1g904I270ZHrOdv9ofwomcE2-HZddFGnxjH2_aw3hjgU0axvS5t4_ow-nHFb8aaHYIhuWfi9-ag1N2TjB83NjP28_kHu9AYch8gQkJXhg2gPR3NO6DDJLzRumtZhMEb_8Psnbju9odPvQNh-qIDSaRJz2BqwQGehum2dTHa1WWjhMpCpWNtrpbCMTnF8opjY8ssEr-7u29pSXRPXWhifa_W093luJ23p70fT6kgxZeT1efE5tHCfJ3iE9CNSjSNUJ71h0tV1N4Ao2l_u0BfnB3h4ybFn3R4VBsgkFDC8H1TXqWAjol-ul2mOLTw8yaxtgrSovEApI0D5faN28lYpgrxG_O2aPy0uTCx3LKRqVbQNEinPpNR2WmIeTcj9U7BpLvQNqowlbVIBfP6SMvzS0wLixup53eegJrD_icoLSej5r0qjm8Aeht0SVZmi1-7_gH5Bqsan525z6Y-679Dl2SbHPwxtts8jcWugUyihrIIIOnRXZe-NLBNZuw-fPuSHKBJPDphaU3IWXrrdVRnUedPWUw3N2pIfin_98Z_LH4DmGhrs0eohdTDU73IQaDX9nN_Ahy6M68TOBWasuul-GWi0HXDJnGs-HyIx2M46njxMyHZ7d5kDjCftKv0-fb_nm8AMk2Py1xbw9R32I3UsjwC3GTs2XvIC0E0jnB2GDmAQ4hMUR9L13XnNi9j41S2Ft1yBewOWO8gowqpOUjehR6GqWPEAohdYzfyzlmCkJeKzxTK2bsj0tJsTVwH6EXx5S4MCsPZvqZVLJUnvxJdjk2yqi4AVWfZxLWuCWKiQdIVlbpM3sYd8YfWpU42Fl7kWqcbJOyjq3CLaAhL8iWja-WRwDc9OShFVf1Ruow73A7zGX1wLiPUENqWQ9KNOaGBOBft-_PydoqMgWIA73a1vTYfCk5jayoD9bm0iew0abOjvdUaL22RMacUajiyLPziYW6V3w5axiSRI0BGwhkKhGRBkFXQG9Z9BeWkRezN8GZR_ybMoESLep96bX5jmlJP8jsV2PNcKAaHKEEYdrTBgbdkcltekhIuwdQPezogpSsiLolKVMHCLKmE2g8aBBz1-wstU6cWsh6S1XNTvWtB9ZDu6X2bVuothOXvC_PeadSuISxOXoWXWduXIaKeqH0Tx4DN_Ysbd4ugyWu4nvijIZliR8ELXKUSA4OKopJfS6ql9m9acgDzKoMdPMtlsblpgK7JdK33Gk5Dpzdq0fDak9TZnnS3xyvkxCAy7e9Us923boN1pLcW3zecYBbJlwkcgATGDlD0b0K6Xq5O67ILLJ6QzD34ABU4T-B0la1Rr_iAEa8VKaCnpsRRc0kehhAxuf2j_kEaG7y393L14Hldw0a58DxSxHLijN5TapWYobdZNY3gUwvuSr1Vr7XHsNdQiYe65J1BH0KsIDp_EW2CjNu0p2oVoP8cXBx0B2JX0E-alzdI7Z-vJ6fjnat8tkdiPAh3Q-_AEWFCb3eHb2q9TYH19ZFyFlak__6cHxP_U0_DP6NvywB67BC7-gCDgkqRB7LFSzVwOE1tRDeY4NMBCp1V-HmK9KFcb5EJuBPLwZ_9tOCN0ciiZIKwJeLnAH0hEvQ-hsxfDvRccji1NJxtMDUrLGCdN4v_2kAAegOQeefP7wZ6Tlrk9a8IAaCXMaVgH8OhTH6POmEoZ3lDkGmGt4HoXGgdlzZ3ovPZqFAiOE","legacy_responseItem_reasoning_16",[1603],{"_45":1604,"_90":700},"**Preparing PR description content**\n\nI'm gathering details from branch changes including .env.example and AGENTS updates to summarize what changed and why, aiming to create a clear PR description without modifying code or creating a new PR.",{"_670":1606,"_662":663,"_672":81},{"_673":1607,"_41":1608,"_694":1609,"_90":696},[1604],"legacy_reasoning_15",[],{"_670":1611,"_662":663,"_672":81},{"_673":1612,"_41":1608,"_694":1613,"_90":696},[],[],{"_670":1615,"_662":663,"_672":81},{"_722":1616,"_724":1617,"_90":726},"call_MzIoohCGQximdHY2TIyw44Dg",{"_408":1618,"_728":-5},"Chunk ID: c8be1d\nWall time: 0.0516 seconds\nProcess exited with code 0\nOriginal token count: 30336\nOutput:\nTotal output lines: 3101\n\ndiff --git a/agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py b/agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py\r\nindex 52f6baf..9b2e2d1 100644\r\n--- a/agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py\r\n+++ b/agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py\r\n@@ -15,31 +15,39 @@ This module is designed to be imported by multi-model evaluation scripts.\r\n \r\n import asyncio\r\n import csv\r\n+import hashlib\r\n+import importlib.util\r\n+import json\r\n import os\r\n import random\r\n+import re\r\n+import sys\r\n+import traceback\r\n+from dataclasses import dataclass\r\n from datetime import datetime\r\n-from typing import Any, Dict, List\r\n+from typing import Any, Awaitable, Callable, Dict, Iterable, List, Optional\r\n \r\n import logfire\r\n from dotenv import load_dotenv\r\n from pydantic import BaseModel, ValidationError\r\n from pydantic_ai import Agent\r\n-from pydantic_ai.exceptions import UsageLimitExceeded, ModelHTTPError\r\n+from pydantic_ai.exceptions import ModelHTTPError, UsageLimitExceeded\r\n from pydantic_ai.mcp import MCPServerStdio\r\n from pydantic_ai.usage import UsageLimits\r\n from pydantic_evals import Case, Dataset\r\n-from pydantic_evals.evaluators import Evaluator, EvaluatorContext, LLMJudge\r\n+from pydantic_evals.evaluators import Evaluator, EvaluatorContext\r\n from pydantic_evals.reporting import EvaluationReport\r\n \r\n from agents_mcp_usage.evaluations.mermaid_evals.mermaid_diagrams import (\r\n invalid_mermaid_diagram_easy,\r\n- invalid_mermaid_diagram_medium,\r\n invalid_mermaid_diagram_hard,\r\n+ invalid_mermaid_diagram_medium,\r\n valid_mermaid_diagram,\r\n )\r\n+from agents_mcp_usage.factory.model_factory import (\r\n+ create_agent as create_agent_with_model,\r\n+)\r\n from agents_mcp_usage.utils import get_mcp_server_path\r\n-import sys\r\n-import importlib.util\r\n \r\n load_dotenv()\r\n \r\n@@ -55,9 +63,15 @@ DEFAULT_MODEL = \"gemini-2.5-pro-preview-06-05\"\r\n \r\n # Retry configuration\r\n RETRYABLE_HTTP_STATUS_CODES = {429, 500, 502, 503, 504}\r\n-MAX_RETRY_ATTEMPTS = 3\r\n-BASE_RETRY_DELAY = 1.0 # seconds\r\n-MAX_RETRY_DELAY = 30.0 # seconds\r\n+\r\n+# Request limit configuration for calls to the model\r\n+REQUEST_LIMIT = 500\r\n+REQUEST_USAGE_COLUMN = \"Requests_Used\"\r\n+\r\n+# Defaults tuned for evaluation runs (avoid hammering endpoints on 429/5xx)\r\n+MAX_RETRY_ATTEMPTS = 20\r\n+BASE_RETRY_DELAY = 2.0 # seconds\r\n+MAX_RETRY_DELAY = 60.0 # seconds\r\n \r\n # Dynamically import validate_mermaid_diagram from the mermaid_validator.py file\r\n mermaid_validator_path = str(get_mcp_server_path(\"mermaid_validator.py\"))\r\n@@ -72,18 +86,93 @@ validate_mermaid_diagram = mermaid_validator.validate_mermaid_diagram\r\n # ============================================================================\r\n \r\n \r\n+class MermaidValidatorUnavailableError(RuntimeError):\r\n+ \"\"\"Raised when Mermaid validation cannot be performed.\r\n+\r\n+ This is treated as *fatal* since it invalidates the evaluation (fail-fast).\r\n+ \"\"\"\r\n+\r\n+\r\n+def _iter_base_exceptions(exception: BaseException) -> Iterable[BaseException]:\r\n+ \"\"\"Yield underlying exceptions, flattening `ExceptionGroup`s.\"\"\"\r\n+ if isinstance(exception, BaseExceptionGroup):\r\n+ for exc in exception.exceptions:\r\n+ yield from _iter_base_exceptions(exc)\r\n+ else:\r\n+ yield exception\r\n+\r\n+\r\n+MERMAID_VALIDATOR_ERROR_HINTS = (\r\n+ \"mermaid_validator\",\r\n+ \"mermaid-validator\",\r\n+ \"validate_mermaid_diagram\",\r\n+)\r\n+\r\n+\r\n+def is_mermaid_validator_unavailable_error(exception: BaseException) -> bool:\r\n+ \"\"\"Best-effort detection for Mermaid validator MCP/server failures.\"\"\"\r\n+ if isinstance(exception, MermaidValidatorUnavailableError):\r\n+ return True\r\n+ text = f\"{type(exception).__name__}: {exception}\".lower()\r\n+ return any(hint in text for hint in MERMAID_VALIDATOR_ERROR_HINTS)\r\n+\r\n+\r\n+def compute_exponential_backoff_delay(\r\n+ attempt: int,\r\n+ base_delay: float = BASE_RETRY_DELAY,\r\n+ max_delay: float = MAX_RETRY_DELAY,\r\n+ jitter: bool = True,\r\n+) -> float:\r\n+ \"\"\"Compute exponential backoff delay for an attempt (0-indexed).\"\"\"\r\n+ delay = min(base_delay * (2**attempt), max_delay)\r\n+ if jitter:\r\n+ delay = delay * (0.5 + random.random() * 0.5) # 50% jitter\r\n+ return delay\r\n+\r\n+\r\n def is_retryable_error(exception: Exception) -> bool:\r\n \"\"\"Checks if an exception is retryable.\r\n \r\n This function checks if the given exception is a retryable HTTP error or a\r\n general connection error.\r\n \r\n- Args:\r\n- exception: The exception to check.\r\n-\r\n- Returns:\r\n- True if the exception is retryable, False otherwise.\r\n+ For `ExceptionGroup`s we inspect contained exceptions and default to\r\n+ retryable *unless* the group clearly contains non-retryable signals (usage\r\n+ limits, per-case timeouts) or a fatal Mermaid validator failure.\r\n \"\"\"\r\n+ if isinstance(exception, MermaidValidatorUnavailableError):\r\n+ return False\r\n+\r\n+ # Per-case timeouts should not be retried (run-level timeouts are handled separately)\r\n+ if isinstance(exception, asyncio.TimeoutError):\r\n+ return False\r\n+\r\n+ if isinstance(exception, BaseExceptionGroup):\r\n+ inner_exceptions = list(_iter_base_exceptions(exception))\r\n+\r\n+ # Fail-fast (fatal)\r\n+ if any(is_mermaid_validator_unavailable_error(exc) for exc in inner_exceptions):\r\n+ return False\r\n+\r\n+ # Non-retryable (policy)\r\n+ if any(isinstance(exc, UsageLimitExceeded) for exc in inner_exceptions):\r\n+ return False\r\n+ if any(isinstance(exc, asyncio.TimeoutError) for exc in inner_exceptions):\r\n+ return False\r\n+\r\n+ # Explicit retryables\r\n+ if any(\r\n+ isinstance(exc, ModelHTTPError)\r\n+ and exc.status_code in RETRYABLE_HTTP_STATUS_CODES\r\n+ for exc in inner_exceptions\r\n+ ):\r\n+ return True\r\n+ if any(isinstance(exc, (ConnectionError, OSError)) for exc in inner_exceptions):\r\n+ return True\r\n+\r\n+ # Default for groups: treat as retryable (matches `error_ExceptionGroup` policy)\r\n+ return True\r\n+\r\n if isinstance(exception, ModelHTTPError):\r\n return exception.status_code in RETRYABLE_HTTP_STATUS_CODES\r\n \r\n@@ -95,11 +184,12 @@ def is_retryable_error(exception: Exception) -> bool:\r\n \r\n \r\n async def exponential_backoff_retry(\r\n- func_call: callable,\r\n+ func_call: Callable[[], Awaitable[Any]],\r\n max_attempts: int = MAX_RETRY_ATTEMPTS,\r\n base_delay: float = BASE_RETRY_DELAY,\r\n max_delay: float = MAX_RETRY_DELAY,\r\n jitter: bool = True,\r\n+ is_retryable: Callable[[Exception], bool] = is_retryable_error,\r\n ) -> Any:\r\n \"\"\"Executes a function with exponential backoff retry logic.\r\n \r\n@@ -127,7 +217,7 @@ async def exponential_backoff_retry(\r\n except Exception as e:\r\n last_exception = e\r\n \r\n- if not is_retryable_error(e):\r\n+ if not is_retryable(e):\r\n logfire.warning(\r\n \"Non-retryable error encountered\",\r\n error_type=type(e).__name__,\r\n@@ -145,10 +235,9 @@ async def exponential_backoff_retry(\r\n )\r\n break\r\n \r\n- # Calculate delay with exponential backoff\r\n- delay = min(base_delay * (2**attempt), max_delay)\r\n- if jitter:\r\n- delay = delay * (0.5 + random.random() * 0.5) # Add 50% jitter\r\n+ delay = compute_exponential_backoff_delay(\r\n+ attempt, base_delay=base_delay, max_delay=max_delay, jitter=jitter\r\n+ )\r\n \r\n logfire.warning(\r\n \"Retryable error encountered, retrying\",\r\n@@ -207,7 +296,7 @@ def create_agent(\r\n \"\"\"Creates an agent with MCP servers for the specified model.\r\n \r\n This function initializes and returns an agent with the necessary MCP\r\n- servers and model settings.\r\n+ servers and model settings using the new model factory.\r\n \r\n Args:\r\n model: The model to use for the agent.\r\n@@ -219,32 +308,9 @@ def create_agent(\r\n if model_settings is None:\r\n model_settings = {}\r\n \r\n- # Handle Bedrock models specifically\r\n- if model.startswith(\"bedrock:\"):\r\n- from pydantic_ai.models.bedrock import BedrockConverseModel\r\n- from pydantic_ai.providers.bedrock import BedrockProvider\r\n-\r\n- # Extract the model name (remove \"bedrock:\" prefix)\r\n- model_name = model.replace(\"bedrock:\", \"\")\r\n-\r\n- # Create BedrockConverseModel with proper region and profile configuration\r\n- bedrock_model = BedrockConverseModel(\r\n- model_name,\r\n- provider=BedrockProvider(\r\n- region_name=os.getenv(\"AWS_REGION\", \"us-east-1\"),\r\n- profile_name=os.getenv(\"AWS_PROFILE\", \"my-aws-profile\"),\r\n- ),\r\n- )\r\n-\r\n- return Agent(\r\n- bedrock_model,\r\n- mcp_servers=get_mcp_servers(),\r\n- model_settings=model_settings,\r\n- )\r\n-\r\n- # For non-Bedrock models, use the original approach\r\n- return Agent(\r\n- model,\r\n+ # Use the new model factory for all models\r\n+ return create_agent_with_model(\r\n+ model=model,\r\n mcp_servers=get_mcp_servers(),\r\n model_settings=model_settings,\r\n )\r\n@@ -259,6 +325,7 @@ class MermaidInput(BaseModel):\r\n \"\"\"Input schema for mermaid diagram fixing.\"\"\"\r\n \r\n invalid_diagram: str\r\n+ case_name: Optional[str] = None\r\n \r\n \r\n class MermaidOutput(BaseModel):\r\n@@ -270,6 +337,184 @@ class MermaidOutput(BaseModel):\r\n tools_used: List[str] = [] # Track which MCP tools were called\r\n \r\n \r\n+# ============================================================================\r\n+# Debug Trace Capture\r\n+# ============================================================================\r\n+\r\n+_SLUG_RE = re.compile(r\"[^a-zA-Z0-9]+\")\r\n+\r\n+\r\n+def _slugify(value: str) -> str:\r\n+ slug = _SLUG_RE.sub(\"_\", value).strip(\"_\")\r\n+ return slug or \"unknown\"\r\n+\r\n+\r\n+def _short_hash(value: str) -> str:\r\n+ return hashlib.sha256(value.encode(\"utf-8\")).hexdigest()[:12]\r\n+\r\n+\r\n+def _usage_limits_to_dict(usage_limits: UsageLimits) -> Dict[str, Any]:\r\n+ data: Dict[str, Any] = {}\r\n+ for key in (\r\n+ \"request_limit\",\r\n+ \"request_tokens_limit\",\r\n+ \"response_tokens_limit\",\r\n+ \"total_tokens_limit\",\r\n+ ):\r\n+ if hasattr(usage_limits, key):\r\n+ value = getattr(usage_limits, key)\r\n+ if value is not None:\r\n+ data[key] = value\r\n+ return data\r\n+\r\n+\r\n+def _usage_to_dict(usage: Any) -> Dict[str, Any]:\r\n+ \"\"\"Best-effort conversion of PydanticAI usage objects to primitives for JSON.\"\"\"\r\n+ details = getattr(usage, \"details\", None) or {}\r\n+ return {\r\n+ \"requests\": getattr(usage, \"requests\", None),\r\n+ \"request_tokens\": getattr(usage, \"request_tokens\", None),\r\n+ \"response_tokens\": getattr(usage, \"response_tokens\", None),\r\n+ \"total_tokens\": getattr(usage, \"total_tokens\", None),\r\n+ \"details\": details,\r\n+ }\r\n+\r\n+\r\n+def _exception_to_dict(exception: BaseException) -> Dict[str, Any]:\r\n+ data: Dict[str, Any] = {\r\n+ \"type\": type(exception).__name__,\r\n+ \"message\": str(exception),\r\n+ \"traceback\": traceback.format_exception(exception),\r\n+ }\r\n+\r\n+ if isinstance(exception, ModelHTTPError):\r\n+ data.update(\r\n+ {\r\n+ \"status_code\": exception.status_code,\r\n+ \"model_name\": exception.model_name,\r\n+ \"body\": exception.body,\r\n+ }\r\n+ )\r\n+\r\n+ if isinstance(exception, BaseExceptionGroup):\r\n+ data[\"exceptions\"] = [_exception_to_dict(e) for e in exception.exceptions]\r\n+\r\n+ return data\r\n+\r\n+\r\n+def _summarize_messages(messages: Any) -> Dict[str, Any]:\r\n+ \"\"\"Create small derived fields to quickly spot tool loops / excessive turn usage.\r\n+\r\n+ Accepts the parsed output of `all_messages_json()`.\r\n+ \"\"\"\r\n+ if not isinstance(messages, list):\r\n+ return {}\r\n+\r\n+ summary: Dict[str, Any] = {\r\n+ \"message_count\": len(messages),\r\n+ \"request_count\": 0,\r\n+ \"response_count\": 0,\r\n+ \"tool_call_count\": 0,\r\n+ \"tool_return_count\": 0,\r\n+ \"tool_names_unique\": [],\r\n+ \"tool_calls_by_name\": {},\r\n+ \"tool_sequence\": [],\r\n+ }\r\n+\r\n+ tools_seen_ordered: List[str] = []\r\n+ tool_calls_by_name: Dict[str, int] = {}\r\n+\r\n+ for msg in messages:\r\n+ if not isinstance(msg, dict):\r\n+ continue\r\n+\r\n+ kind = msg.get(\"kind\")\r\n+ if kind == \"request\":\r\n+ summary[\"request_count\"] += 1\r\n+ elif kind == \"response\":\r\n+ summary[\"response_count\"] += 1\r\n+\r\n+ parts = msg.get(\"parts\") or []\r\n+ if not isinstance(parts, list):\r\n+ continue\r\n+\r\n+ for part in parts:\r\n+ if not isinstance(part, dict):\r\n+ continue\r\n+ part_kind = part.get(\"part_kind\")\r\n+ tool_name = part.get(\"tool_name\")\r\n+ tool_call_id = part.get(\"tool_call_id\")\r\n+\r\n+ if tool_name and tool_name not in tools_seen_ordered:\r\n+ tools_seen_ordered.append(tool_name)\r\n+\r\n+ if part_kind in {\"tool-call\", \"builtin-tool-call\"}:\r\n+ summary[\"tool_call_count\"] += 1\r\n+ if tool_name:\r\n+ tool_calls_by_name[tool_name] = tool_calls_by_name.get(tool_name, 0) + 1\r\n+ summary[\"tool_sequence\"].append(\r\n+ {\"event\": \"call\", \"tool_name\": tool_name, \"tool_call_id\": tool_call_id}\r\n+ )\r\n+\r\n+ if part_kind in {\"tool-return\", \"builtin-tool-return\"}:\r\n+ summary[\"tool_return_count\"] += 1\r\n+ summary[\"tool_sequence\"].append(\r\n+ {\"event\": \"return\", \"tool_name\": tool_name, \"tool_call_id\": tool_call_id}\r\n+ )\r\n+\r\n+ summary[\"tool_names_unique\"] = tools_seen_ordered\r\n+ summary[\"tool_calls_by_name\"] = tool_calls_by_name\r\n+ return summary\r\n+\r\n+\r\n+@dataclass(slots=True)\r\n+class MermaidEvalTraceContext:\r\n+ \"\"\"Per-case debug trace capture config passed down from evaluation runners.\"\"\"\r\n+\r\n+ enabled: bool\r\n+ trace_dir: str\r\n+ model: str\r\n+ planned_run_index: Optional[int] = None # 0-based planned run index\r\n+ run_attempt: Optional[int] = None # 1-based attempt counter at runner-level\r\n+ evaluation_name: Optional[str] = None\r\n+\r\n+\r\n+def _build_trace_path(trace_ctx: MermaidEvalTraceContext, inputs: MermaidInput) -> str:\r\n+ model_slug = _slugify(trace_ctx.model)\r\n+ case_slug = _slugify(inputs.case_name) if inputs.case_name else f\"hash_{_short_hash(inputs.invalid_diagram)}\"\r\n+ run_part = (\r\n+ f\"run{(trace_ctx.planned_run_index + 1):03d}\"\r\n+ if trace_ctx.planned_run_index is not None\r\n+ else \"run000\"\r\n+ )\r\n+ attempt_part = (\r\n+ f\"attempt{trace_ctx.run_attempt:03d}\"\r\n+ if trace_ctx.run_attempt is not None\r\n+ else \"attempt000\"\r\n+ )\r\n+\r\n+ model_dir = os.path.join(trace_ctx.trace_dir, model_slug)\r\n+ filename = f\"model={model_slug}__{run_part}__{attempt_part}__case={case_slug}.json\"\r\n+ return os.path.join(model_dir, filename)\r\n+\r\n+\r\n+def _write_trace(trace_path: str, trace_record: Dict[str, Any]) -> None:\r\n+ \"\"\"Best-effort writer; never raise (debug traces should not break evals).\"\"\"\r\n+ try:\r\n+ os.makedirs(os.path.dirname(trace_path), exist_ok=True)\r\n+ tmp_path = f\"{trace_path}.tmp\"\r\n+ with open(tmp_path, \"w\", encoding=\"utf-8\") as f:\r\n+ json.dump(trace_record, f, indent=2, ensure_ascii=False, sort_keys=False)\r\n+ os.replace(tmp_path, trace_path)\r\n+ except Exception as e: # pragma: no cover\r\n+ logfire.warning(\r\n+ \"Failed to write debug trace file\",\r\n+ trace_path=trace_path,\r\n+ error_type=type(e).__name__,\r\n+ error=str(e),\r\n+ )\r\n+\r\n+\r\n # ============================================================================\r\n # Custom Evaluators\r\n # ============================================================================\r\n@@ -389,13 +634,25 @@ class MermaidDiagramValid(Evaluator[MermaidInput, MermaidOutput]):\r\n result = await exponential_backoff_retry(\r\n lambda: validate_mermaid_diagram(input_str)\r\n )\r\n+ except MermaidValidatorUnavailableError:\r\n+ raise\r\n except Exception as e:\r\n logfire.error(\r\n \"Failed to validate mermaid diagram after retries\",\r\n error_type=type(e).__name__,\r\n error_message=str(e),\r\n )\r\n- return 0.0\r\n+ raise MermaidValidatorUnavailableError(\r\n+ \"Mermaid validator MCP server unavailable\"\r\n+ ) from e\r\n+\r\n+ if (\r\n+ not result.is_valid\r\n+ and result.error_message\r\n+ and result.error_message.startswith(\"Error validating mermaid diagram:\")\r\n+ ):\r\n+ # If the validator itself is failing, results are invalid → fail-fast.\r\n+ raise MermaidValidatorUnavailableError(result.error_message)\r\n \r\n if result.is_valid:\r\n logfire.info(\"Mermaid diagram validation succeeded\")\r\n@@ -413,54 +670,159 @@ class MermaidDiagramValid(Evaluator[MermaidInput, MermaidOutput]):\r\n \r\n \r\n async def fix_mermaid_diagram(\r\n- inputs: MermaidInput, model: str = DEFAULT_MODEL\r\n+ inputs: MermaidInput,\r\n+ model: str = DEFAULT_MODEL,\r\n+ *,\r\n+ trace_ctx: MermaidEvalTraceContext | None = None,\r\n ) -> MermaidOutput:\r\n \"\"\"Fixes an invalid mermaid diagram using an agent with multiple MCP servers.\r\n \r\n- This function runs an agent to fix a given mermaid diagram, handling\r\n- various exceptions and capturing metrics.\r\n+ In normal mode, we return aggregate usage metrics and tool names.\r\n+ In debug trace mode, we additionally write a per-case JSON file containing:\r\n+ input, prompt, full message history (incl. tool call args + tool return payloads),\r\n+ and usage.\r\n \r\n Args:\r\n inputs: The input containing the invalid diagram.\r\n model: The model to use for the agent.\r\n+ trace_ctx: Optional debug trace config (used by multi-model runner).\r\n \r\n Returns:\r\n A MermaidOutput object with the fixed diagram and captured metrics.\r\n \"\"\"\r\n- query = f\"Add the current time and fix the mermaid diagram syntax using the validator: {inputs.invalid_diagram}. Return only the fixed mermaid diagram between backticks.\"\r\n+ query = (\r\n+ \"Add the current time and fix the mermaid diagram syntax using the validator: \"\r\n+ f\"{inputs.invalid_diagram}. Return only the fixed mermaid diagram between backticks.\"\r\n+ )\r\n+\r\n+ trace_enabled = bool(trace_ctx and trace_ctx.enabled)\r\n \r\n # Create a fresh agent for each invocation to avoid concurrent usage issues\r\n current_agent = create_agent(model)\r\n- usage_limits = UsageLimits(request_limit=5)\r\n+\r\n+ # NOTE: request_limit is the most common limiter we hit in tool-loop failure modes.\r\n+ usage_limits = UsageLimits(request_limit=REQUEST_LIMIT)\r\n+\r\n+ captured_messages_json: str | None = None\r\n+ captured_usage: Any | None = None\r\n+ captured_agent_run_id: str | None = None\r\n+\r\n+ def _maybe_write_trace(\r\n+ *,\r\n+ status: str,\r\n+ failure_reason: str,\r\n+ output_text: str | None,\r\n+ extracted_diagram: str | None,\r\n+ exception: BaseException | None = None,\r\n+ ) -> None:\r\n+ nonlocal captured_messages_json, captured_usage, captured_agent_run_id\r\n+\r\n+ if not trace_enabled or trace_ctx is None:\r\n+ return\r\n+\r\n+ trace_path = _build_trace_path(trace_ctx, inputs)\r\n+\r\n+ messages_parsed: Any = None\r\n+ if captured_messages_json:\r\n+ try:\r\n+ messages_parsed = json.loads(captured_messages_json)\r\n+ except …20336 tokens truncated…test_group\",\r\n+ \"Duration\",\r\n+ \"Score_MermaidDiagramValid\",\r\n+ \"Score_UsageLimitNotExceeded\",\r\n+ \"Score_UsedBothMCPTools\",\r\n+ \"total_tokens\",\r\n+ \"provider\",\r\n+ \"Metric_request_tokens\",\r\n+ \"Metric_response_tokens\",\r\n+ \"total_cost\",\r\n+ \"input_cost\",\r\n+ \"output_cost\",\r\n+ ]\r\n+\r\n+ # Add any missing columns in the expected raw_data schema\r\n+ for col in raw_columns:\r\n+ if col not in df.columns:\r\n+ df[col] = 0 if col in numeric_defaults else None\r\n+\r\n+ raw_data_records = df[raw_columns].to_dict(orient=\"records\")\r\n+\r\n+ return {\r\n+ \"stats\": stats,\r\n+ \"leaderboard\": leaderboard.to_dict(orient=\"records\"),\r\n+ \"pareto_data\": pareto_data.to_dict(orient=\"records\"),\r\n+ \"test_groups_data\": test_groups_data.to_dict(orient=\"records\"),\r\n+ \"failure_analysis_data\": failure_analysis_data,\r\n+ \"cost_breakdown_data\": cost_breakdown_data.to_dict(orient=\"records\"),\r\n+ \"raw_data\": raw_data_records,\r\n+ \"config\": config or {},\r\n+ }\r\n+\r\n+\r\n+def summarise_input_file(path: Path, data: Dict[str, Any]) -> InputFileSummary:\r\n+ raw = data.get(\"raw_data\", [])\r\n+ models = sorted({row.get(\"Model\", \"\") for row in raw if \"Model\" in row})\r\n+ cases = sorted({row.get(\"Case\", \"\") for row in raw if \"Case\" in row})\r\n+\r\n+ return InputFileSummary(\r\n+ path=str(path),\r\n+ total_runs=len(raw),\r\n+ models=models,\r\n+ test_cases=cases,\r\n+ )\r\n+\r\n+\r\n+def build_merge_report(\r\n+ input_summaries: List[InputFileSummary],\r\n+ dedup_strategy: DedupStrategy,\r\n+ df_all: pd.DataFrame,\r\n+ df_merged: pd.DataFrame,\r\n+) -> Dict[str, Any]:\r\n+ key_cols = [\"Model\", \"Case\"]\r\n+\r\n+ total_input_runs = int(len(df_all))\r\n+ unique_runs_before = int(df_all.drop_duplicates(subset=key_cols).shape[0]) if not df_all.empty else 0\r\n+ unique_runs_after = int(df_merged.drop_duplicates(subset=key_cols).shape[0]) if not df_merged.empty else 0\r\n+\r\n+ duplicates_before = total_input_runs - unique_runs_before\r\n+ duplicates_removed = unique_runs_before - unique_runs_after\r\n+\r\n+ summary = {\r\n+ \"merged_at\": datetime.utcnow().isoformat() + \"Z\",\r\n+ \"dedup_strategy\": dedup_strategy,\r\n+ \"input_files\": [\r\n+ {\r\n+ \"path\": s.path,\r\n+ \"total_runs\": s.total_runs,\r\n+ \"models\": s.models,\r\n+ \"test_cases\": s.test_cases,\r\n+ }\r\n+ for s in input_summaries\r\n+ ],\r\n+ \"summary\": {\r\n+ \"total_input_runs\": total_input_runs,\r\n+ \"unique_runs_before_dedup\": unique_runs_before,\r\n+ \"unique_runs_after_dedup\": unique_runs_after,\r\n+ \"duplicate_runs_detected\": duplicates_before,\r\n+ \"duplicate_runs_removed\": max(0, duplicates_removed),\r\n+ },\r\n+ \"models_merged\": sorted(df_merged[\"Model\"].dropna().unique().tolist()) if not df_merged.empty else [],\r\n+ }\r\n+\r\n+ return summary\r\n+\r\n+\r\n+def parse_args(argv: Optional[List[str]] = None) -> argparse.Namespace:\r\n+ parser = argparse.ArgumentParser(\r\n+ description=\"Merge multiple Merbench processed JSON result files.\",\r\n+ )\r\n+\r\n+ parser.add_argument(\r\n+ \"-i\",\r\n+ \"--input\",\r\n+ nargs=\"+\",\r\n+ dest=\"input_files\",\r\n+ required=True,\r\n+ help=\"Input JSON files produced by preprocess_merbench_data.py\",\r\n+ )\r\n+\r\n+ parser.add_argument(\r\n+ \"-o\",\r\n+ \"--output\",\r\n+ dest=\"output_file\",\r\n+ required=True,\r\n+ help=\"Path to write merged JSON output\",\r\n+ )\r\n+\r\n+ parser.add_argument(\r\n+ \"--dedup\",\r\n+ dest=\"dedup_strategy\",\r\n+ choices=[\"keep-all\", \"keep-first\", \"keep-last\", \"average\"],\r\n+ default=\"keep-all\",\r\n+ help=(\r\n+ \"Deduplication strategy for duplicate (Model, Case) combinations: \"\r\n+ \"keep-all (default), keep-first, keep-last, average\"\r\n+ ),\r\n+ )\r\n+\r\n+ parser.add_argument(\r\n+ \"--report\",\r\n+ dest=\"report_file\",\r\n+ help=\"Optional path to write a detailed merge report JSON\",\r\n+ )\r\n+\r\n+ parser.add_argument(\r\n+ \"--verbose\",\r\n+ action=\"store_true\",\r\n+ help=\"Enable verbose logging\",\r\n+ )\r\n+\r\n+ return parser.parse_args(argv)\r\n+\r\n+\r\n+def main(argv: Optional[List[str]] = None) -> None:\r\n+ args = parse_args(argv)\r\n+\r\n+ project_root = get_project_root()\r\n+\r\n+ # Resolve file paths relative to project root if not absolute\r\n+ input_paths: List[Path] = []\r\n+ for p in args.input_files:\r\n+ path = Path(p)\r\n+ if not path.is_absolute():\r\n+ path = project_root / path\r\n+ input_paths.append(path)\r\n+\r\n+ output_path = Path(args.output_file)\r\n+ if not output_path.is_absolute():\r\n+ output_path = project_root / output_path\r\n+\r\n+ report_path: Optional[Path] = None\r\n+ if args.report_file:\r\n+ report_path = Path(args.report_file)\r\n+ if not report_path.is_absolute():\r\n+ report_path = project_root / report_path\r\n+\r\n+ if args.verbose:\r\n+ print(\"Merging Merbench benchmark results...\")\r\n+ print(f\"- Deduplication strategy: {args.dedup_strategy}\")\r\n+ print(\"- Input files:\")\r\n+ for p in input_paths:\r\n+ print(f\" - {p}\")\r\n+ print(f\"- Output file: {output_path}\")\r\n+ if report_path:\r\n+ print(f\"- Report file: {report_path}\")\r\n+\r\n+ # Load all datasets\r\n+ datasets: List[Dict[str, Any]] = []\r\n+ input_summaries: List[InputFileSummary] = []\r\n+\r\n+ for path in input_paths:\r\n+ data = load_json_file(path, verbose=args.verbose)\r\n+ datasets.append(data)\r\n+ input_summaries.append(summarise_input_file(path, data))\r\n+\r\n+ # Choose a canonical config from the first dataset (if any)\r\n+ config: Dict[str, Any] = {}\r\n+ if datasets and isinstance(datasets[0].get(\"config\"), dict):\r\n+ config = datasets[0][\"config\"]\r\n+\r\n+ # Merge raw_data and build aggregates\r\n+ df_all = pd.DataFrame([row for data in datasets for row in data.get(\"raw_data\", [])])\r\n+ df_merged = merge_raw_data(datasets, dedup_strategy=args.dedup_strategy, verbose=args.verbose)\r\n+\r\n+ if args.verbose:\r\n+ print(f\"Total input runs: {len(df_all)}\")\r\n+ print(f\"Merged runs after dedup ({args.dedup_strategy}): {len(df_merged)}\")\r\n+\r\n+ merged_data = build_aggregates(df_merged, config=config)\r\n+\r\n+ # Write merged JSON output\r\n+ output_path.parent.mkdir(parents=True, exist_ok=True)\r\n+ with output_path.open(\"w\", encoding=\"utf-8\") as f:\r\n+ json.dump(merged_data, f, indent=2)\r\n+\r\n+ if args.verbose:\r\n+ print(f\"Merged results written to {output_path}\")\r\n+\r\n+ # Optionally write a merge report\r\n+ if report_path is not None:\r\n+ report = build_merge_report(\r\n+ input_summaries=input_summaries,\r\n+ dedup_strategy=args.dedup_strategy,\r\n+ df_all=df_all,\r\n+ df_merged=df_merged,\r\n+ )\r\n+\r\n+ report_path.parent.mkdir(parents=True, exist_ok=True)\r\n+ with report_path.open(\"w\", encoding=\"utf-8\") as f:\r\n+ json.dump(report, f, indent=2)\r\n+\r\n+ if args.verbose:\r\n+ print(f\"Merge report written to {report_path}\")\r\n+\r\n+\r\n+if __name__ == \"__main__\": # pragma: no cover\r\n+ main()\r\ndiff --git a/agents_mcp_usage/evaluations/mermaid_evals/scripts/preprocess_merbench_data.py b/agents_mcp_usage/evaluations/mermaid_evals/scripts/preprocess_merbench_data.py\r\nindex 8def71d..0194740 100644\r\n--- a/agents_mcp_usage/evaluations/mermaid_evals/scripts/preprocess_merbench_data.py\r\n+++ b/agents_mcp_usage/evaluations/mermaid_evals/scripts/preprocess_merbench_data.py\r\n@@ -1,19 +1,49 @@\r\n #!/usr/bin/env python3\r\n-import pandas as pd\r\n+import argparse\r\n import json\r\n+import re\r\n import sys\r\n-import argparse\r\n from datetime import datetime\r\n from pathlib import Path\r\n-from typing import Dict, List, Any\r\n+from typing import Any, Dict, List\r\n+\r\n+import pandas as pd\r\n \r\n # Add parent directory to path to import modules\r\n sys.path.append(str(Path(__file__).parent.parent))\r\n \r\n from agents_mcp_usage.evaluations.mermaid_evals.dashboard_config import DEFAULT_CONFIG\r\n from agents_mcp_usage.evaluations.mermaid_evals.schemas import DashboardConfig\r\n+from agents_mcp_usage.evaluations.mermaid_evals.evals_pydantic_mcp import (\r\n+ REQUEST_USAGE_COLUMN,\r\n+)\r\n from agents_mcp_usage.utils import get_project_root\r\n \r\n+\r\n+REASONING_SUFFIX_RE = re.compile(r\"\\s*\\((low|medium|high)\\)\\s*$\", re.IGNORECASE)\r\n+\r\n+\r\n+def normalize_model_name(model_name: Any) -> Any:\r\n+ \"\"\"Normalize model names for cost lookup.\r\n+\r\n+ - Strip trailing reasoning hints like \"(medium)\".\r\n+ - Map OpenAI Responses provider prefix to the standard OpenAI key used in costs.json.\r\n+ - Add an \"openai:\" prefix for bare GPT model names.\r\n+ \"\"\"\r\n+\r\n+ if not isinstance(model_name, str):\r\n+ return model_name\r\n+\r\n+ name = model_name.strip()\r\n+ name = REASONING_SUFFIX_RE.sub(\"\", name).strip()\r\n+\r\n+ if name.startswith(\"openai-responses:\"):\r\n+ name = \"openai:\" + name.split(\":\", 1)[1]\r\n+ elif name.startswith(\"gpt-\") and \":\" not in name:\r\n+ name = f\"openai:{name}\"\r\n+\r\n+ return name\r\n+\r\n def load_model_costs(file_path: Path) -> Dict[str, Any]:\r\n \"\"\"Load model costs from JSON file.\"\"\"\r\n with open(file_path, 'r') as f:\r\n@@ -63,8 +93,9 @@ def calculate_costs(df: pd.DataFrame, cost_config: Dict, config: DashboardConfig\r\n continue\r\n \r\n model = row.get(\"Model\")\r\n- model_costs = cost_config.get(model)\r\n- \r\n+ model_key = normalize_model_name(model)\r\n+ model_costs = cost_config.get(model_key)\r\n+\r\n if not model_costs:\r\n continue\r\n \r\n@@ -159,11 +190,20 @@ def process_csv_for_static_site(csv_path):\r\n # Read CSV\r\n df = pd.read_csv(csv_path)\r\n \r\n- # Replace NaN values with 0 for numeric columns\r\n- numeric_columns = ['Metric_request_tokens', 'Metric_response_tokens', 'Metric_total_tokens']\r\n- for col in numeric_columns:\r\n- if col in df.columns:\r\n- df[col] = df[col].fillna(0)\r\n+ if REQUEST_USAGE_COLUMN not in df.columns:\r\n+ df[REQUEST_USAGE_COLUMN] = \"na\"\r\n+ df[REQUEST_USAGE_COLUMN] = df[REQUEST_USAGE_COLUMN].fillna(\"na\")\r\n+ \r\n+ # Normalize token columns so missing usage is treated as 0 and types are numeric.\r\n+ cost_calc_config = config.cost_calculation\r\n+ input_token_cols = cost_calc_config.input_token_cols\r\n+ output_token_cols = cost_calc_config.output_token_cols\r\n+ token_cols = input_token_cols + output_token_cols\r\n+\r\n+ for col in token_cols:\r\n+ if col not in df.columns:\r\n+ df[col] = 0\r\n+ df[col] = pd.to_numeric(df[col], errors=\"coerce\").fillna(0)\r\n \r\n # Extract grouping column (test case types)\r\n df['test_group'] = df['Case'].apply(lambda x: x.split('_')[-1] if '_' in x else 'other')\r\n@@ -176,27 +216,61 @@ def process_csv_for_static_site(csv_path):\r\n else:\r\n df[\"thinking_tokens\"] = 0\r\n df[\"text_tokens\"] = 0\r\n- \r\n- # Calculate total tokens\r\n- df[\"total_tokens\"] = df[\"Metric_total_tokens\"].fillna(0)\r\n+\r\n+ df[\"thinking_tokens\"] = pd.to_numeric(df[\"thinking_tokens\"], errors=\"coerce\").fillna(0)\r\n+ df[\"text_tokens\"] = pd.to_numeric(df[\"text_tokens\"], errors=\"coerce\").fillna(0)\r\n+\r\n+ # Calculate total tokens in the same way as the local Streamlit dashboard:\r\n+ # sum of input + output token columns (including thinking tokens).\r\n+ df[\"total_tokens\"] = 0\r\n+ for col in token_cols:\r\n+ df[col] = pd.to_numeric(df[col], errors=\"coerce\").fillna(0)\r\n+ df[\"total_tokens\"] += df[col]\r\n \r\n # Calculate success rate (primary metric)\r\n df[\"Success_Rate\"] = df[\"Score_MermaidDiagramValid\"] * 100\r\n \r\n # Extract provider from model name\r\n- def extract_provider(model_name):\r\n- if model_name.startswith(\"gemini-\"):\r\n+ def extract_provider(model_name: str) -> str:\r\n+ name = str(model_name)\r\n+\r\n+ if name.startswith(\"openrouter:\"):\r\n+ openrouter_id = name.split(\":\", 1)[1]\r\n+ provider_id = (\r\n+ openrouter_id.split(\"/\", 1)[0] if \"/\" in openrouter_id else openrouter_id\r\n+ )\r\n+ provider_key = provider_id.strip().lower()\r\n+\r\n+ provider_map = {\r\n+ \"amazon\": \"Amazon\",\r\n+ \"anthropic\": \"Anthropic\",\r\n+ \"cohere\": \"Cohere\",\r\n+ \"deepseek\": \"DeepSeek\",\r\n+ \"google\": \"Google\",\r\n+ \"meta-llama\": \"Meta\",\r\n+ \"mistralai\": \"Mistral\",\r\n+ \"moonshotai\": \"MoonshotAI\",\r\n+ \"openai\": \"OpenAI\",\r\n+ \"qwen\": \"Qwen\",\r\n+ \"x-ai\": \"xAI\",\r\n+ }\r\n+ if provider_key in provider_map:\r\n+ return provider_map[provider_key]\r\n+\r\n+ fallback = provider_id.replace(\"-\", \" \").replace(\"_\", \" \").strip()\r\n+ return fallback.title() if fallback else \"OpenRouter\"\r\n+\r\n+ if name.startswith(\"gemini-\"):\r\n return \"Google\"\r\n- elif \"nova\" in model_name.lower():\r\n+ if \"nova\" in name.lower():\r\n return \"Amazon\"\r\n- elif \"claude\" in model_name.lower():\r\n+ if \"claude\" in name.lower():\r\n return \"Anthropic\"\r\n- elif \"gpt\" in model_name.lower():\r\n+ if \"gpt\" in name.lower():\r\n return \"OpenAI\"\r\n- elif model_name.startswith(\"o\"):\r\n+ if name.startswith(\"o\"):\r\n return \"OpenAI\"\r\n- else:\r\n- return \"Other\"\r\n+ return \"Other\"\r\n \r\n df[\"provider\"] = df[\"Model\"].apply(extract_provider)\r\n \r\ndiff --git a/agents_mcp_usage/evaluations/mermaid_evals/scripts/verify_retry_orchestration.py b/agents_mcp_usage/evaluations/mermaid_evals/scripts/verify_retry_orchestration.py\r\nnew file mode 100644\r\nindex 0000000..716184e\r\n--- /dev/null\r\n+++ b/agents_mcp_usage/evaluations/mermaid_evals/scripts/verify_retry_orchestration.py\r\n@@ -0,0 +1,126 @@\r\n+#!/usr/bin/env python3\r\n+\"\"\"\r\n+Offline verification for run-level retry + backoff orchestration.\r\n+\r\n+This script uses a stub Dataset to simulate:\r\n+- evaluation timeouts (retryable at run-level)\r\n+- report-based retry triggers (e.g. http_error_429, error_ExceptionGroup)\r\n+- non-retry conditions (usage_limit_exceeded, agent_timeout)\r\n+- fatal conditions (Mermaid validator unavailable)\r\n+\r\n+It patches delays to zero so it runs quickly and deterministically.\r\n+\"\"\"\r\n+\r\n+from __future__ import annotations\r\n+\r\n+import asyncio\r\n+from dataclasses import dataclass, field\r\n+from typing import Any, List, Sequence\r\n+\r\n+import agents_mcp_usage.evaluations.mermaid_evals.run_multi_evals as run_multi_evals\r\n+from agents_mcp_usage.evaluations.mermaid_evals.evals_pydantic_mcp import (\r\n+ MermaidValidatorUnavailableError,\r\n+)\r\n+from agents_mcp_usage.evaluations.mermaid_evals.run_multi_evals import MultiModelEvaluator\r\n+\r\n+\r\n+@dataclass(slots=True)\r\n+class StubOutput:\r\n+ failure_reason: str = \"\"\r\n+ fixed_diagram: str = \"\"\r\n+ metrics: dict[str, Any] = field(default_factory=dict)\r\n+ tools_used: list[str] = field(default_factory=list)\r\n+\r\n+\r\n+@dataclass(slots=True)\r\n+class StubCase:\r\n+ name: str\r\n+ task_duration: float = 0.0\r\n+ output: StubOutput = field(default_factory=StubOutput)\r\n+ scores: dict[str, Any] = field(default_factory=dict)\r\n+\r\n+\r\n+@dataclass(slots=True)\r\n+class StubReport:\r\n+ cases: list[StubCase]\r\n+\r\n+\r\n+class StubDataset:\r\n+ \"\"\"A minimal stub that matches the Dataset.evaluate() shape we need.\"\"\"\r\n+\r\n+ def __init__(self, sequence: Sequence[object]) -> None:\r\n+ self.sequence = list(sequence)\r\n+ self.calls = 0\r\n+\r\n+ async def evaluate(self, *_args: Any, **_kwargs: Any) -> object:\r\n+ item = self.sequence[self.calls]\r\n+ self.calls += 1\r\n+ if isinstance(item, BaseException):\r\n+ raise item\r\n+ return item\r\n+\r\n+\r\n+async def _no_sleep(_delay: float) -> None:\r\n+ return None\r\n+\r\n+\r\n+def _make_report(failure_reasons: List[str]) -> StubReport:\r\n+ cases = [\r\n+ StubCase(name=f\"case_{idx}\", output=StubOutput(failure_reason=reason))\r\n+ for idx, reason in enumerate(failure_reasons)\r\n+ ]\r\n+ return StubReport(cases=cases)\r\n+\r\n+\r\n+async def main() -> None:\r\n+ # Patch delay + sleep so retries are fast in this script\r\n+ run_multi_evals.compute_exponential_backoff_delay = lambda *_a, **_k: 0.0 # type: ignore[assignment]\r\n+ run_multi_evals.asyncio.sleep = _no_sleep # type: ignore[assignment]\r\n+\r\n+ model = \"test-model\"\r\n+\r\n+ # 1) Evaluation timeout should retry up to MAX_RETRY_ATTEMPTS, then fail.\r\n+ evaluator = MultiModelEvaluator(models=[model], judge_model=\"judge\", output_dir=\"./mermaid_eval_results\")\r\n+ dataset = StubDataset([asyncio.TimeoutError()] * run_multi_evals.MAX_RETRY_ATTEMPTS)\r\n+ result = await evaluator.run_single_evaluation(model, 0, dataset, timeout=0.01)\r\n+ assert result is None\r\n+ assert evaluator.results[model].failed_runs[0][\"attempts\"] == run_multi_evals.MAX_RETRY_ATTEMPTS\r\n+\r\n+ # 2) Retryable report failure_reason should trigger a retry, then succeed.\r\n+ evaluator = MultiModelEvaluator(models=[model], judge_model=\"judge\", output_dir=\"./mermaid_eval_results\")\r\n+ dataset = StubDataset([_make_report([\"http_error_429\"]), _make_report([\"\"])])\r\n+ result = await evaluator.run_single_evaluation(model, 0, dataset, timeout=1)\r\n+ assert result is not None\r\n+ assert result.attempts == 2\r\n+ assert dataset.calls == 2\r\n+\r\n+ # 3) usage_limit_exceeded should NOT trigger a retry.\r\n+ evaluator = MultiModelEvaluator(models=[model], judge_model=\"judge\", output_dir=\"./mermaid_eval_results\")\r\n+ dataset = StubDataset([_make_report([\"usage_limit_exceeded\"]), _make_report([\"\"])])\r\n+ result = await evaluator.run_single_evaluation(model, 0, dataset, timeout=1)\r\n+ assert result is not None\r\n+ assert result.attempts == 1\r\n+ assert dataset.calls == 1\r\n+\r\n+ # 4) agent_timeout should NOT trigger a retry.\r\n+ evaluator = MultiModelEvaluator(models=[model], judge_model=\"judge\", output_dir=\"./mermaid_eval_results\")\r\n+ dataset = StubDataset([_make_report([\"agent_timeout\"]), _make_report([\"\"])])\r\n+ result = await evaluator.run_single_evaluation(model, 0, dataset, timeout=1)\r\n+ assert result is not None\r\n+ assert result.attempts == 1\r\n+ assert dataset.calls == 1\r\n+\r\n+ # 5) Mermaid validator unavailability should fail-fast (raise).\r\n+ evaluator = MultiModelEvaluator(models=[model], judge_model=\"judge\", output_dir=\"./mermaid_eval_results\")\r\n+ dataset = StubDataset([MermaidValidatorUnavailableError(\"validator down\")])\r\n+ try:\r\n+ await evaluator.run_single_evaluation(model, 0, dataset, timeout=1)\r\n+ raise AssertionError(\"Expected MermaidValidatorUnavailableError\")\r\n+ except MermaidValidatorUnavailableError:\r\n+ pass\r\n+\r\n+ print(\"verify_retry_orchestration.py: OK\")\r\n+\r\n+\r\n+if __name__ == \"__main__\":\r\n+ asyncio.run(main())\r\n\\ No newline at end of file\r\ndiff --git a/agents_mcp_usage/factory/__init__.py b/agents_mcp_usage/factory/__init__.py\r\nnew file mode 100644\r\nindex 0000000..132677e\r\n--- /dev/null\r\n+++ b/agents_mcp_usage/factory/__init__.py\r\n@@ -0,0 +1 @@\r\n+\"\"\"Utils module for agents MCP usage.\"\"\"\r\ndiff --git a/mcp_servers/mermaid_validator.py b/mcp_servers/mermaid_validator.py\r\nindex 2a9a9fc..665f85c 100644\r\n--- a/mcp_servers/mermaid_validator.py\r\n+++ b/mcp_servers/mermaid_validator.py\r\n@@ -66,7 +66,7 @@ async def validate_mermaid_diagram(diagram_text: str) -> MermaidValidationResult\r\n [\r\n \"npx\",\r\n \"-y\",\r\n- \"@mermaid-js/mermaid-cli\",\r\n+ \"@mermaid-js/mermaid-cli@11.4.2\",\r\n \"-i\",\r\n temp_file_path,\r\n \"-o\",\r\n",{"_670":1620,"_662":663,"_672":81},{"_731":1621,"_733":1622,"_735":1623,"_737":738,"_739":740,"_741":31,"_41":1616,"_742":1624,"_744":745,"_667":111,"_90":746},"Total output lines: 3101\n\ndiff --git a/agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py b/agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py\r\nindex 52f6baf..9b2e2d1 100644\r\n--- a/agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py\r\n+++ b/agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py\r\n@@ -15,31 +15,39 @@ This module is designed to be imported by multi-model evaluation scripts.\r\n \r\n import asyncio\r\n import csv\r\n+import hashlib\r\n+import importlib.util\r\n+import json\r\n import os\r\n import random\r\n+import re\r\n+import sys\r\n+import traceback\r\n+from dataclasses import dataclass\r\n from datetime import datetime\r\n-from typing import Any, Dict, List\r\n+from typing import Any, Awaitable, Callable, Dict, Iterable, List, Optional\r\n \r\n import logfire\r\n from dotenv import load_dotenv\r\n from pydantic import BaseModel, ValidationError\r\n from pydantic_ai import Agent\r\n-from pydantic_ai.exceptions import UsageLimitExceeded, ModelHTTPError\r\n+from pydantic_ai.exceptions import ModelHTTPError, UsageLimitExceeded\r\n from pydantic_ai.mcp import MCPServerStdio\r\n from pydantic_ai.usage import UsageLimits\r\n from pydantic_evals import Case, Dataset\r\n-from pydantic_evals.evaluators import Evaluator, EvaluatorContext, LLMJudge\r\n+from pydantic_evals.evaluators import Evaluator, EvaluatorContext\r\n from pydantic_evals.reporting import EvaluationReport\r\n \r\n from agents_mcp_usage.evaluations.mermaid_evals.mermaid_diagrams import (\r\n invalid_mermaid_diagram_easy,\r\n- invalid_mermaid_diagram_medium,\r\n invalid_mermaid_diagram_hard,\r\n+ invalid_mermaid_diagram_medium,\r\n valid_mermaid_diagram,\r\n )\r\n+from agents_mcp_usage.factory.model_factory import (\r\n+ create_agent as create_agent_with_model,\r\n+)\r\n from agents_mcp_usage.utils import get_mcp_server_path\r\n-import sys\r\n-import importlib.util\r\n \r\n load_dotenv()\r\n \r\n@@ -55,9 +63,15 @@ DEFAULT_MODEL = \"gemini-2.5-pro-preview-06-05\"\r\n \r\n # Retry configuration\r\n RETRYABLE_HTTP_STATUS_CODES = {429, 500, 502, 503, 504}\r\n-MAX_RETRY_ATTEMPTS = 3\r\n-BASE_RETRY_DELAY = 1.0 # seconds\r\n-MAX_RETRY_DELAY = 30.0 # seconds\r\n+\r\n+# Request limit configuration for calls to the model\r\n+REQUEST_LIMIT = 500\r\n+REQUEST_USAGE_COLUMN = \"Requests_Used\"\r\n+\r\n+# Defaults tuned for evaluation runs (avoid hammering endpoints on 429/5xx)\r\n+MAX_RETRY_ATTEMPTS = 20\r\n+BASE_RETRY_DELAY = 2.0 # seconds\r\n+MAX_RETRY_DELAY = 60.0 # seconds\r\n \r\n # Dynamically import validate_mermaid_diagram from the mermaid_validator.py file\r\n mermaid_validator_path = str(get_mcp_server_path(\"mermaid_validator.py\"))\r\n@@ -72,18 +86,93 @@ validate_mermaid_diagram = mermaid_validator.validate_mermaid_diagram\r\n # ============================================================================\r\n \r\n \r\n+class MermaidValidatorUnavailableError(RuntimeError):\r\n+ \"\"\"Raised when Mermaid validation cannot be performed.\r\n+\r\n+ This is treated as *fatal* since it invalidates the evaluation (fail-fast).\r\n+ \"\"\"\r\n+\r\n+\r\n+def _iter_base_exceptions(exception: BaseException) -> Iterable[BaseException]:\r\n+ \"\"\"Yield underlying exceptions, flattening `ExceptionGroup`s.\"\"\"\r\n+ if isinstance(exception, BaseExceptionGroup):\r\n+ for exc in exception.exceptions:\r\n+ yield from _iter_base_exceptions(exc)\r\n+ else:\r\n+ yield exception\r\n+\r\n+\r\n+MERMAID_VALIDATOR_ERROR_HINTS = (\r\n+ \"mermaid_validator\",\r\n+ \"mermaid-validator\",\r\n+ \"validate_mermaid_diagram\",\r\n+)\r\n+\r\n+\r\n+def is_mermaid_validator_unavailable_error(exception: BaseException) -> bool:\r\n+ \"\"\"Best-effort detection for Mermaid validator MCP/server failures.\"\"\"\r\n+ if isinstance(exception, MermaidValidatorUnavailableError):\r\n+ return True\r\n+ text = f\"{type(exception).__name__}: {exception}\".lower()\r\n+ return any(hint in text for hint in MERMAID_VALIDATOR_ERROR_HINTS)\r\n+\r\n+\r\n+def compute_exponential_backoff_delay(\r\n+ attempt: int,\r\n+ base_delay: float = BASE_RETRY_DELAY,\r\n+ max_delay: float = MAX_RETRY_DELAY,\r\n+ jitter: bool = True,\r\n+) -> float:\r\n+ \"\"\"Compute exponential backoff delay for an attempt (0-indexed).\"\"\"\r\n+ delay = min(base_delay * (2**attempt), max_delay)\r\n+ if jitter:\r\n+ delay = delay * (0.5 + random.random() * 0.5) # 50% jitter\r\n+ return delay\r\n+\r\n+\r\n def is_retryable_error(exception: Exception) -> bool:\r\n \"\"\"Checks if an exception is retryable.\r\n \r\n This function checks if the given exception is a retryable HTTP error or a\r\n general connection error.\r\n \r\n- Args:\r\n- exception: The exception to check.\r\n-\r\n- Returns:\r\n- True if the exception is retryable, False otherwise.\r\n+ For `ExceptionGroup`s we inspect contained exceptions and default to\r\n+ retryable *unless* the group clearly contains non-retryable signals (usage\r\n+ limits, per-case timeouts) or a fatal Mermaid validator failure.\r\n \"\"\"\r\n+ if isinstance(exception, MermaidValidatorUnavailableError):\r\n+ return False\r\n+\r\n+ # Per-case timeouts should not be retried (run-level timeouts are handled separately)\r\n+ if isinstance(exception, asyncio.TimeoutError):\r\n+ return False\r\n+\r\n+ if isinstance(exception, BaseExceptionGroup):\r\n+ inner_exceptions = list(_iter_base_exceptions(exception))\r\n+\r\n+ # Fail-fast (fatal)\r\n+ if any(is_mermaid_validator_unavailable_error(exc) for exc in inner_exceptions):\r\n+ return False\r\n+\r\n+ # Non-retryable (policy)\r\n+ if any(isinstance(exc, UsageLimitExceeded) for exc in inner_exceptions):\r\n+ return False\r\n+ if any(isinstance(exc, asyncio.TimeoutError) for exc in inner_exceptions):\r\n+ return False\r\n+\r\n+ # Explicit retryables\r\n+ if any(\r\n+ isinstance(exc, ModelHTTPError)\r\n+ and exc.status_code in RETRYABLE_HTTP_STATUS_CODES\r\n+ for exc in inner_exceptions\r\n+ ):\r\n+ return True\r\n+ if any(isinstance(exc, (ConnectionError, OSError)) for exc in inner_exceptions):\r\n+ return True\r\n+\r\n+ # Default for groups: treat as retryable (matches `error_ExceptionGroup` policy)\r\n+ return True\r\n+\r\n if isinstance(exception, ModelHTTPError):\r\n return exception.status_code in RETRYABLE_HTTP_STATUS_CODES\r\n \r\n@@ -95,11 +184,12 @@ def is_retryable_error(exception: Exception) -> bool:\r\n \r\n \r\n async def exponential_backoff_retry(\r\n- func_call: callable,\r\n+ func_call: Callable[[], Awaitable[Any]],\r\n max_attempts: int = MAX_RETRY_ATTEMPTS,\r\n base_delay: float = BASE_RETRY_DELAY,\r\n max_delay: float = MAX_RETRY_DELAY,\r\n jitter: bool = True,\r\n+ is_retryable: Callable[[Exception], bool] = is_retryable_error,\r\n ) -> Any:\r\n \"\"\"Executes a function with exponential backoff retry logic.\r\n \r\n@@ -127,7 +217,7 @@ async def exponential_backoff_retry(\r\n except Exception as e:\r\n last_exception = e\r\n \r\n- if not is_retryable_error(e):\r\n+ if not is_retryable(e):\r\n logfire.warning(\r\n \"Non-retryable error encountered\",\r\n error_type=type(e).__name__,\r\n@@ -145,10 +235,9 @@ async def exponential_backoff_retry(\r\n )\r\n break\r\n \r\n- # Calculate delay with exponential backoff\r\n- delay = min(base_delay * (2**attempt), max_delay)\r\n- if jitter:\r\n- delay = delay * (0.5 + random.random() * 0.5) # Add 50% jitter\r\n+ delay = compute_exponential_backoff_delay(\r\n+ attempt, base_delay=base_delay, max_delay=max_delay, jitter=jitter\r\n+ )\r\n \r\n logfire.warning(\r\n \"Retryable error encountered, retrying\",\r\n@@ -207,7 +296,7 @@ def create_agent(\r\n \"\"\"Creates an agent with MCP servers for the specified model.\r\n \r\n This function initializes and returns an agent with the necessary MCP\r\n- servers and model settings.\r\n+ servers and model settings using the new model factory.\r\n \r\n Args:\r\n model: The model to use for the agent.\r\n@@ -219,32 +308,9 @@ def create_agent(\r\n if model_settings is None:\r\n model_settings = {}\r\n \r\n- # Handle Bedrock models specifically\r\n- if model.startswith(\"bedrock:\"):\r\n- from pydantic_ai.models.bedrock import BedrockConverseModel\r\n- from pydantic_ai.providers.bedrock import BedrockProvider\r\n-\r\n- # Extract the model name (remove \"bedrock:\" prefix)\r\n- model_name = model.replace(\"bedrock:\", \"\")\r\n-\r\n- # Create BedrockConverseModel with proper region and profile configuration\r\n- bedrock_model = BedrockConverseModel(\r\n- model_name,\r\n- provider=BedrockProvider(\r\n- region_name=os.getenv(\"AWS_REGION\", \"us-east-1\"),\r\n- profile_name=os.getenv(\"AWS_PROFILE\", \"my-aws-profile\"),\r\n- ),\r\n- )\r\n-\r\n- return Agent(\r\n- bedrock_model,\r\n- mcp_servers=get_mcp_servers(),\r\n- model_settings=model_settings,\r\n- )\r\n-\r\n- # For non-Bedrock models, use the original approach\r\n- return Agent(\r\n- model,\r\n+ # Use the new model factory for all models\r\n+ return create_agent_with_model(\r\n+ model=model,\r\n mcp_servers=get_mcp_servers(),\r\n model_settings=model_settings,\r\n )\r\n@@ -259,6 +325,7 @@ class MermaidInput(BaseModel):\r\n \"\"\"Input schema for mermaid diagram fixing.\"\"\"\r\n \r\n invalid_diagram: str\r\n+ case_name: Optional[str] = None\r\n \r\n \r\n class MermaidOutput(BaseModel):\r\n@@ -270,6 +337,184 @@ class MermaidOutput(BaseModel):\r\n tools_used: List[str] = [] # Track which MCP tools were called\r\n \r\n \r\n+# ============================================================================\r\n+# Debug Trace Capture\r\n+# ============================================================================\r\n+\r\n+_SLUG_RE = re.compile(r\"[^a-zA-Z0-9]+\")\r\n+\r\n+\r\n+def _slugify(value: str) -> str:\r\n+ slug = _SLUG_RE.sub(\"_\", value).strip(\"_\")\r\n+ return slug or \"unknown\"\r\n+\r\n+\r\n+def _short_hash(value: str) -> str:\r\n+ return hashlib.sha256(value.encode(\"utf-8\")).hexdigest()[:12]\r\n+\r\n+\r\n+def _usage_limits_to_dict(usage_limits: UsageLimits) -> Dict[str, Any]:\r\n+ data: Dict[str, Any] = {}\r\n+ for key in (\r\n+ \"request_limit\",\r\n+ \"request_tokens_limit\",\r\n+ \"response_tokens_limit\",\r\n+ \"total_tokens_limit\",\r\n+ ):\r\n+ if hasattr(usage_limits, key):\r\n+ value = getattr(usage_limits, key)\r\n+ if value is not None:\r\n+ data[key] = value\r\n+ return data\r\n+\r\n+\r\n+def _usage_to_dict(usage: Any) -> Dict[str, Any]:\r\n+ \"\"\"Best-effort conversion of PydanticAI usage objects to primitives for JSON.\"\"\"\r\n+ details = getattr(usage, \"details\", None) or {}\r\n+ return {\r\n+ \"requests\": getattr(usage, \"requests\", None),\r\n+ \"request_tokens\": getattr(usage, \"request_tokens\", None),\r\n+ \"response_tokens\": getattr(usage, \"response_tokens\", None),\r\n+ \"total_tokens\": getattr(usage, \"total_tokens\", None),\r\n+ \"details\": details,\r\n+ }\r\n+\r\n+\r\n+def _exception_to_dict(exception: BaseException) -> Dict[str, Any]:\r\n+ data: Dict[str, Any] = {\r\n+ \"type\": type(exception).__name__,\r\n+ \"message\": str(exception),\r\n+ \"traceback\": traceback.format_exception(exception),\r\n+ }\r\n+\r\n+ if isinstance(exception, ModelHTTPError):\r\n+ data.update(\r\n+ {\r\n+ \"status_code\": exception.status_code,\r\n+ \"model_name\": exception.model_name,\r\n+ \"body\": exception.body,\r\n+ }\r\n+ )\r\n+\r\n+ if isinstance(exception, BaseExceptionGroup):\r\n+ data[\"exceptions\"] = [_exception_to_dict(e) for e in exception.exceptions]\r\n+\r\n+ return data\r\n+\r\n+\r\n+def _summarize_messages(messages: Any) -> Dict[str, Any]:\r\n+ \"\"\"Create small derived fields to quickly spot tool loops / excessive turn usage.\r\n+\r\n+ Accepts the parsed output of `all_messages_json()`.\r\n+ \"\"\"\r\n+ if not isinstance(messages, list):\r\n+ return {}\r\n+\r\n+ summary: Dict[str, Any] = {\r\n+ \"message_count\": len(messages),\r\n+ \"request_count\": 0,\r\n+ \"response_count\": 0,\r\n+ \"tool_call_count\": 0,\r\n+ \"tool_return_count\": 0,\r\n+ \"tool_names_unique\": [],\r\n+ \"tool_calls_by_name\": {},\r\n+ \"tool_sequence\": [],\r\n+ }\r\n+\r\n+ tools_seen_ordered: List[str] = []\r\n+ tool_calls_by_name: Dict[str, int] = {}\r\n+\r\n+ for msg in messages:\r\n+ if not isinstance(msg, dict):\r\n+ continue\r\n+\r\n+ kind = msg.get(\"kind\")\r\n+ if kind == \"request\":\r\n+ summary[\"request_count\"] += 1\r\n+ elif kind == \"response\":\r\n+ summary[\"response_count\"] += 1\r\n+\r\n+ parts = msg.get(\"parts\") or []\r\n+ if not isinstance(parts, list):\r\n+ continue\r\n+\r\n+ for part in parts:\r\n+ if not isinstance(part, dict):\r\n+ continue\r\n+ part_kind = part.get(\"part_kind\")\r\n+ tool_name = part.get(\"tool_name\")\r\n+ tool_call_id = part.get(\"tool_call_id\")\r\n+\r\n+ if tool_name and tool_name not in tools_seen_ordered:\r\n+ tools_seen_ordered.append(tool_name)\r\n+\r\n+ if part_kind in {\"tool-call\", \"builtin-tool-call\"}:\r\n+ summary[\"tool_call_count\"] += 1\r\n+ if tool_name:\r\n+ tool_calls_by_name[tool_name] = tool_calls_by_name.get(tool_name, 0) + 1\r\n+ summary[\"tool_sequence\"].append(\r\n+ {\"event\": \"call\", \"tool_name\": tool_name, \"tool_call_id\": tool_call_id}\r\n+ )\r\n+\r\n+ if part_kind in {\"tool-return\", \"builtin-tool-return\"}:\r\n+ summary[\"tool_return_count\"] += 1\r\n+ summary[\"tool_sequence\"].append(\r\n+ {\"event\": \"return\", \"tool_name\": tool_name, \"tool_call_id\": tool_call_id}\r\n+ )\r\n+\r\n+ summary[\"tool_names_unique\"] = tools_seen_ordered\r\n+ summary[\"tool_calls_by_name\"] = tool_calls_by_name\r\n+ return summary\r\n+\r\n+\r\n+@dataclass(slots=True)\r\n+class MermaidEvalTraceContext:\r\n+ \"\"\"Per-case debug trace capture config passed down from evaluation runners.\"\"\"\r\n+\r\n+ enabled: bool\r\n+ trace_dir: str\r\n+ model: str\r\n+ planned_run_index: Optional[int] = None # 0-based planned run index\r\n+ run_attempt: Optional[int] = None # 1-based attempt counter at runner-level\r\n+ evaluation_name: Optional[str] = None\r\n+\r\n+\r\n+def _build_trace_path(trace_ctx: MermaidEvalTraceContext, inputs: MermaidInput) -> str:\r\n+ model_slug = _slugify(trace_ctx.model)\r\n+ case_slug = _slugify(inputs.case_name) if inputs.case_name else f\"hash_{_short_hash(inputs.invalid_diagram)}\"\r\n+ run_part = (\r\n+ f\"run{(trace_ctx.planned_run_index + 1):03d}\"\r\n+ if trace_ctx.planned_run_index is not None\r\n+ else \"run000\"\r\n+ )\r\n+ attempt_part = (\r\n+ f\"attempt{trace_ctx.run_attempt:03d}\"\r\n+ if trace_ctx.run_attempt is not None\r\n+ else \"attempt000\"\r\n+ )\r\n+\r\n+ model_dir = os.path.join(trace_ctx.trace_dir, model_slug)\r\n+ filename = f\"model={model_slug}__{run_part}__{attempt_part}__case={case_slug}.json\"\r\n+ return os.path.join(model_dir, filename)\r\n+\r\n+\r\n+def _write_trace(trace_path: str, trace_record: Dict[str, Any]) -> None:\r\n+ \"\"\"Best-effort writer; never raise (debug traces should not break evals).\"\"\"\r\n+ try:\r\n+ os.makedirs(os.path.dirname(trace_path), exist_ok=True)\r\n+ tmp_path = f\"{trace_path}.tmp\"\r\n+ with open(tmp_path, \"w\", encoding=\"utf-8\") as f:\r\n+ json.dump(trace_record, f, indent=2, ensure_ascii=False, sort_keys=False)\r\n+ os.replace(tmp_path, trace_path)\r\n+ except Exception as e: # pragma: no cover\r\n+ logfire.warning(\r\n+ \"Failed to write debug trace file\",\r\n+ trace_path=trace_path,\r\n+ error_type=type(e).__name__,\r\n+ error=str(e),\r\n+ )\r\n+\r\n+\r\n # ============================================================================\r\n # Custom Evaluators\r\n # ============================================================================\r\n@@ -389,13 +634,25 @@ class MermaidDiagramValid(Evaluator[MermaidInput, MermaidOutput]):\r\n result = await exponential_backoff_retry(\r\n lambda: validate_mermaid_diagram(input_str)\r\n )\r\n+ except MermaidValidatorUnavailableError:\r\n+ raise\r\n except Exception as e:\r\n logfire.error(\r\n \"Failed to validate mermaid diagram after retries\",\r\n error_type=type(e).__name__,\r\n error_message=str(e),\r\n )\r\n- return 0.0\r\n+ raise MermaidValidatorUnavailableError(\r\n+ \"Mermaid validator MCP server unavailable\"\r\n+ ) from e\r\n+\r\n+ if (\r\n+ not result.is_valid\r\n+ and result.error_message\r\n+ and result.error_message.startswith(\"Error validating mermaid diagram:\")\r\n+ ):\r\n+ # If the validator itself is failing, results are invalid → fail-fast.\r\n+ raise MermaidValidatorUnavailableError(result.error_message)\r\n \r\n if result.is_valid:\r\n logfire.info(\"Mermaid diagram validation succeeded\")\r\n@@ -413,54 +670,159 @@ class MermaidDiagramValid(Evaluator[MermaidInput, MermaidOutput]):\r\n \r\n \r\n async def fix_mermaid_diagram(\r\n- inputs: MermaidInput, model: str = DEFAULT_MODEL\r\n+ inputs: MermaidInput,\r\n+ model: str = DEFAULT_MODEL,\r\n+ *,\r\n+ trace_ctx: MermaidEvalTraceContext | None = None,\r\n ) -> MermaidOutput:\r\n \"\"\"Fixes an invalid mermaid diagram using an agent with multiple MCP servers.\r\n \r\n- This function runs an agent to fix a given mermaid diagram, handling\r\n- various exceptions and capturing metrics.\r\n+ In normal mode, we return aggregate usage metrics and tool names.\r\n+ In debug trace mode, we additionally write a per-case JSON file containing:\r\n+ input, prompt, full message history (incl. tool call args + tool return payloads),\r\n+ and usage.\r\n \r\n Args:\r\n inputs: The input containing the invalid diagram.\r\n model: The model to use for the agent.\r\n+ trace_ctx: Optional debug trace config (used by multi-model runner).\r\n \r\n Returns:\r\n A MermaidOutput object with the fixed diagram and captured metrics.\r\n \"\"\"\r\n- query = f\"Add the current time and fix the mermaid diagram syntax using the validator: {inputs.invalid_diagram}. Return only the fixed mermaid diagram between backticks.\"\r\n+ query = (\r\n+ \"Add the current time and fix the mermaid diagram syntax using the validator: \"\r\n+ f\"{inputs.invalid_diagram}. Return only the fixed mermaid diagram between backticks.\"\r\n+ )\r\n+\r\n+ trace_enabled = bool(trace_ctx and trace_ctx.enabled)\r\n \r\n # Create a fresh agent for each invocation to avoid concurrent usage issues\r\n current_agent = create_agent(model)\r\n- usage_limits = UsageLimits(request_limit=5)\r\n+\r\n+ # NOTE: request_limit is the most common limiter we hit in tool-loop failure modes.\r\n+ usage_limits = UsageLimits(request_limit=REQUEST_LIMIT)\r\n+\r\n+ captured_messages_json: str | None = None\r\n+ captured_usage: Any | None = None\r\n+ captured_agent_run_id: str | None = None\r\n+\r\n+ def _maybe_write_trace(\r\n+ *,\r\n+ status: str,\r\n+ failure_reason: str,\r\n+ output_text: str | None,\r\n+ extracted_diagram: str | None,\r\n+ exception: BaseException | None = None,\r\n+ ) -> None:\r\n+ nonlocal captured_messages_json, captured_usage, captured_agent_run_id\r\n+\r\n+ if not trace_enabled or trace_ctx is None:\r\n+ return\r\n+\r\n+ trace_path = _build_trace_path(trace_ctx, inputs)\r\n+\r\n+ messages_parsed: Any = None\r\n+ if captured_messages_json:\r\n+ try:\r\n+ messages_parsed = json.loads(captured_messages_json)\r\n+ except …20336 tokens truncated…test_group\",\r\n+ \"Duration\",\r\n+ \"Score_MermaidDiagramValid\",\r\n+ \"Score_UsageLimitNotExceeded\",\r\n+ \"Score_UsedBothMCPTools\",\r\n+ \"total_tokens\",\r\n+ \"provider\",\r\n+ \"Metric_request_tokens\",\r\n+ \"Metric_response_tokens\",\r\n+ \"total_cost\",\r\n+ \"input_cost\",\r\n+ \"output_cost\",\r\n+ ]\r\n+\r\n+ # Add any missing columns in the expected raw_data schema\r\n+ for col in raw_columns:\r\n+ if col not in df.columns:\r\n+ df[col] = 0 if col in numeric_defaults else None\r\n+\r\n+ raw_data_records = df[raw_columns].to_dict(orient=\"records\")\r\n+\r\n+ return {\r\n+ \"stats\": stats,\r\n+ \"leaderboard\": leaderboard.to_dict(orient=\"records\"),\r\n+ \"pareto_data\": pareto_data.to_dict(orient=\"records\"),\r\n+ \"test_groups_data\": test_groups_data.to_dict(orient=\"records\"),\r\n+ \"failure_analysis_data\": failure_analysis_data,\r\n+ \"cost_breakdown_data\": cost_breakdown_data.to_dict(orient=\"records\"),\r\n+ \"raw_data\": raw_data_records,\r\n+ \"config\": config or {},\r\n+ }\r\n+\r\n+\r\n+def summarise_input_file(path: Path, data: Dict[str, Any]) -> InputFileSummary:\r\n+ raw = data.get(\"raw_data\", [])\r\n+ models = sorted({row.get(\"Model\", \"\") for row in raw if \"Model\" in row})\r\n+ cases = sorted({row.get(\"Case\", \"\") for row in raw if \"Case\" in row})\r\n+\r\n+ return InputFileSummary(\r\n+ path=str(path),\r\n+ total_runs=len(raw),\r\n+ models=models,\r\n+ test_cases=cases,\r\n+ )\r\n+\r\n+\r\n+def build_merge_report(\r\n+ input_summaries: List[InputFileSummary],\r\n+ dedup_strategy: DedupStrategy,\r\n+ df_all: pd.DataFrame,\r\n+ df_merged: pd.DataFrame,\r\n+) -> Dict[str, Any]:\r\n+ key_cols = [\"Model\", \"Case\"]\r\n+\r\n+ total_input_runs = int(len(df_all))\r\n+ unique_runs_before = int(df_all.drop_duplicates(subset=key_cols).shape[0]) if not df_all.empty else 0\r\n+ unique_runs_after = int(df_merged.drop_duplicates(subset=key_cols).shape[0]) if not df_merged.empty else 0\r\n+\r\n+ duplicates_before = total_input_runs - unique_runs_before\r\n+ duplicates_removed = unique_runs_before - unique_runs_after\r\n+\r\n+ summary = {\r\n+ \"merged_at\": datetime.utcnow().isoformat() + \"Z\",\r\n+ \"dedup_strategy\": dedup_strategy,\r\n+ \"input_files\": [\r\n+ {\r\n+ \"path\": s.path,\r\n+ \"total_runs\": s.total_runs,\r\n+ \"models\": s.models,\r\n+ \"test_cases\": s.test_cases,\r\n+ }\r\n+ for s in input_summaries\r\n+ ],\r\n+ \"summary\": {\r\n+ \"total_input_runs\": total_input_runs,\r\n+ \"unique_runs_before_dedup\": unique_runs_before,\r\n+ \"unique_runs_after_dedup\": unique_runs_after,\r\n+ \"duplicate_runs_detected\": duplicates_before,\r\n+ \"duplicate_runs_removed\": max(0, duplicates_removed),\r\n+ },\r\n+ \"models_merged\": sorted(df_merged[\"Model\"].dropna().unique().tolist()) if not df_merged.empty else [],\r\n+ }\r\n+\r\n+ return summary\r\n+\r\n+\r\n+def parse_args(argv: Optional[List[str]] = None) -> argparse.Namespace:\r\n+ parser = argparse.ArgumentParser(\r\n+ description=\"Merge multiple Merbench processed JSON result files.\",\r\n+ )\r\n+\r\n+ parser.add_argument(\r\n+ \"-i\",\r\n+ \"--input\",\r\n+ nargs=\"+\",\r\n+ dest=\"input_files\",\r\n+ required=True,\r\n+ help=\"Input JSON files produced by preprocess_merbench_data.py\",\r\n+ )\r\n+\r\n+ parser.add_argument(\r\n+ \"-o\",\r\n+ \"--output\",\r\n+ dest=\"output_file\",\r\n+ required=True,\r\n+ help=\"Path to write merged JSON output\",\r\n+ )\r\n+\r\n+ parser.add_argument(\r\n+ \"--dedup\",\r\n+ dest=\"dedup_strategy\",\r\n+ choices=[\"keep-all\", \"keep-first\", \"keep-last\", \"average\"],\r\n+ default=\"keep-all\",\r\n+ help=(\r\n+ \"Deduplication strategy for duplicate (Model, Case) combinations: \"\r\n+ \"keep-all (default), keep-first, keep-last, average\"\r\n+ ),\r\n+ )\r\n+\r\n+ parser.add_argument(\r\n+ \"--report\",\r\n+ dest=\"report_file\",\r\n+ help=\"Optional path to write a detailed merge report JSON\",\r\n+ )\r\n+\r\n+ parser.add_argument(\r\n+ \"--verbose\",\r\n+ action=\"store_true\",\r\n+ help=\"Enable verbose logging\",\r\n+ )\r\n+\r\n+ return parser.parse_args(argv)\r\n+\r\n+\r\n+def main(argv: Optional[List[str]] = None) -> None:\r\n+ args = parse_args(argv)\r\n+\r\n+ project_root = get_project_root()\r\n+\r\n+ # Resolve file paths relative to project root if not absolute\r\n+ input_paths: List[Path] = []\r\n+ for p in args.input_files:\r\n+ path = Path(p)\r\n+ if not path.is_absolute():\r\n+ path = project_root / path\r\n+ input_paths.append(path)\r\n+\r\n+ output_path = Path(args.output_file)\r\n+ if not output_path.is_absolute():\r\n+ output_path = project_root / output_path\r\n+\r\n+ report_path: Optional[Path] = None\r\n+ if args.report_file:\r\n+ report_path = Path(args.report_file)\r\n+ if not report_path.is_absolute():\r\n+ report_path = project_root / report_path\r\n+\r\n+ if args.verbose:\r\n+ print(\"Merging Merbench benchmark results...\")\r\n+ print(f\"- Deduplication strategy: {args.dedup_strategy}\")\r\n+ print(\"- Input files:\")\r\n+ for p in input_paths:\r\n+ print(f\" - {p}\")\r\n+ print(f\"- Output file: {output_path}\")\r\n+ if report_path:\r\n+ print(f\"- Report file: {report_path}\")\r\n+\r\n+ # Load all datasets\r\n+ datasets: List[Dict[str, Any]] = []\r\n+ input_summaries: List[InputFileSummary] = []\r\n+\r\n+ for path in input_paths:\r\n+ data = load_json_file(path, verbose=args.verbose)\r\n+ datasets.append(data)\r\n+ input_summaries.append(summarise_input_file(path, data))\r\n+\r\n+ # Choose a canonical config from the first dataset (if any)\r\n+ config: Dict[str, Any] = {}\r\n+ if datasets and isinstance(datasets[0].get(\"config\"), dict):\r\n+ config = datasets[0][\"config\"]\r\n+\r\n+ # Merge raw_data and build aggregates\r\n+ df_all = pd.DataFrame([row for data in datasets for row in data.get(\"raw_data\", [])])\r\n+ df_merged = merge_raw_data(datasets, dedup_strategy=args.dedup_strategy, verbose=args.verbose)\r\n+\r\n+ if args.verbose:\r\n+ print(f\"Total input runs: {len(df_all)}\")\r\n+ print(f\"Merged runs after dedup ({args.dedup_strategy}): {len(df_merged)}\")\r\n+\r\n+ merged_data = build_aggregates(df_merged, config=config)\r\n+\r\n+ # Write merged JSON output\r\n+ output_path.parent.mkdir(parents=True, exist_ok=True)\r\n+ with output_path.open(\"w\", encoding=\"utf-8\") as f:\r\n+ json.dump(merged_data, f, indent=2)\r\n+\r\n+ if args.verbose:\r\n+ print(f\"Merged results written to {output_path}\")\r\n+\r\n+ # Optionally write a merge report\r\n+ if report_path is not None:\r\n+ report = build_merge_report(\r\n+ input_summaries=input_summaries,\r\n+ dedup_strategy=args.dedup_strategy,\r\n+ df_all=df_all,\r\n+ df_merged=df_merged,\r\n+ )\r\n+\r\n+ report_path.parent.mkdir(parents=True, exist_ok=True)\r\n+ with report_path.open(\"w\", encoding=\"utf-8\") as f:\r\n+ json.dump(report, f, indent=2)\r\n+\r\n+ if args.verbose:\r\n+ print(f\"Merge report written to {report_path}\")\r\n+\r\n+\r\n+if __name__ == \"__main__\": # pragma: no cover\r\n+ main()\r\ndiff --git a/agents_mcp_usage/evaluations/mermaid_evals/scripts/preprocess_merbench_data.py b/agents_mcp_usage/evaluations/mermaid_evals/scripts/preprocess_merbench_data.py\r\nindex 8def71d..0194740 100644\r\n--- a/agents_mcp_usage/evaluations/mermaid_evals/scripts/preprocess_merbench_data.py\r\n+++ b/agents_mcp_usage/evaluations/mermaid_evals/scripts/preprocess_merbench_data.py\r\n@@ -1,19 +1,49 @@\r\n #!/usr/bin/env python3\r\n-import pandas as pd\r\n+import argparse\r\n import json\r\n+import re\r\n import sys\r\n-import argparse\r\n from datetime import datetime\r\n from pathlib import Path\r\n-from typing import Dict, List, Any\r\n+from typing import Any, Dict, List\r\n+\r\n+import pandas as pd\r\n \r\n # Add parent directory to path to import modules\r\n sys.path.append(str(Path(__file__).parent.parent))\r\n \r\n from agents_mcp_usage.evaluations.mermaid_evals.dashboard_config import DEFAULT_CONFIG\r\n from agents_mcp_usage.evaluations.mermaid_evals.schemas import DashboardConfig\r\n+from agents_mcp_usage.evaluations.mermaid_evals.evals_pydantic_mcp import (\r\n+ REQUEST_USAGE_COLUMN,\r\n+)\r\n from agents_mcp_usage.utils import get_project_root\r\n \r\n+\r\n+REASONING_SUFFIX_RE = re.compile(r\"\\s*\\((low|medium|high)\\)\\s*$\", re.IGNORECASE)\r\n+\r\n+\r\n+def normalize_model_name(model_name: Any) -> Any:\r\n+ \"\"\"Normalize model names for cost lookup.\r\n+\r\n+ - Strip trailing reasoning hints like \"(medium)\".\r\n+ - Map OpenAI Responses provider prefix to the standard OpenAI key used in costs.json.\r\n+ - Add an \"openai:\" prefix for bare GPT model names.\r\n+ \"\"\"\r\n+\r\n+ if not isinstance(model_name, str):\r\n+ return model_name\r\n+\r\n+ name = model_name.strip()\r\n+ name = REASONING_SUFFIX_RE.sub(\"\", name).strip()\r\n+\r\n+ if name.startswith(\"openai-responses:\"):\r\n+ name = \"openai:\" + name.split(\":\", 1)[1]\r\n+ elif name.startswith(\"gpt-\") and \":\" not in name:\r\n+ name = f\"openai:{name}\"\r\n+\r\n+ return name\r\n+\r\n def load_model_costs(file_path: Path) -> Dict[str, Any]:\r\n \"\"\"Load model costs from JSON file.\"\"\"\r\n with open(file_path, 'r') as f:\r\n@@ -63,8 +93,9 @@ def calculate_costs(df: pd.DataFrame, cost_config: Dict, config: DashboardConfig\r\n continue\r\n \r\n model = row.get(\"Model\")\r\n- model_costs = cost_config.get(model)\r\n- \r\n+ model_key = normalize_model_name(model)\r\n+ model_costs = cost_config.get(model_key)\r\n+\r\n if not model_costs:\r\n continue\r\n \r\n@@ -159,11 +190,20 @@ def process_csv_for_static_site(csv_path):\r\n # Read CSV\r\n df = pd.read_csv(csv_path)\r\n \r\n- # Replace NaN values with 0 for numeric columns\r\n- numeric_columns = ['Metric_request_tokens', 'Metric_response_tokens', 'Metric_total_tokens']\r\n- for col in numeric_columns:\r\n- if col in df.columns:\r\n- df[col] = df[col].fillna(0)\r\n+ if REQUEST_USAGE_COLUMN not in df.columns:\r\n+ df[REQUEST_USAGE_COLUMN] = \"na\"\r\n+ df[REQUEST_USAGE_COLUMN] = df[REQUEST_USAGE_COLUMN].fillna(\"na\")\r\n+ \r\n+ # Normalize token columns so missing usage is treated as 0 and types are numeric.\r\n+ cost_calc_config = config.cost_calculation\r\n+ input_token_cols = cost_calc_config.input_token_cols\r\n+ output_token_cols = cost_calc_config.output_token_cols\r\n+ token_cols = input_token_cols + output_token_cols\r\n+\r\n+ for col in token_cols:\r\n+ if col not in df.columns:\r\n+ df[col] = 0\r\n+ df[col] = pd.to_numeric(df[col], errors=\"coerce\").fillna(0)\r\n \r\n # Extract grouping column (test case types)\r\n df['test_group'] = df['Case'].apply(lambda x: x.split('_')[-1] if '_' in x else 'other')\r\n@@ -176,27 +216,61 @@ def process_csv_for_static_site(csv_path):\r\n else:\r\n df[\"thinking_tokens\"] = 0\r\n df[\"text_tokens\"] = 0\r\n- \r\n- # Calculate total tokens\r\n- df[\"total_tokens\"] = df[\"Metric_total_tokens\"].fillna(0)\r\n+\r\n+ df[\"thinking_tokens\"] = pd.to_numeric(df[\"thinking_tokens\"], errors=\"coerce\").fillna(0)\r\n+ df[\"text_tokens\"] = pd.to_numeric(df[\"text_tokens\"], errors=\"coerce\").fillna(0)\r\n+\r\n+ # Calculate total tokens in the same way as the local Streamlit dashboard:\r\n+ # sum of input + output token columns (including thinking tokens).\r\n+ df[\"total_tokens\"] = 0\r\n+ for col in token_cols:\r\n+ df[col] = pd.to_numeric(df[col], errors=\"coerce\").fillna(0)\r\n+ df[\"total_tokens\"] += df[col]\r\n \r\n # Calculate success rate (primary metric)\r\n df[\"Success_Rate\"] = df[\"Score_MermaidDiagramValid\"] * 100\r\n \r\n # Extract provider from model name\r\n- def extract_provider(model_name):\r\n- if model_name.startswith(\"gemini-\"):\r\n+ def extract_provider(model_name: str) -> str:\r\n+ name = str(model_name)\r\n+\r\n+ if name.startswith(\"openrouter:\"):\r\n+ openrouter_id = name.split(\":\", 1)[1]\r\n+ provider_id = (\r\n+ openrouter_id.split(\"/\", 1)[0] if \"/\" in openrouter_id else openrouter_id\r\n+ )\r\n+ provider_key = provider_id.strip().lower()\r\n+\r\n+ provider_map = {\r\n+ \"amazon\": \"Amazon\",\r\n+ \"anthropic\": \"Anthropic\",\r\n+ \"cohere\": \"Cohere\",\r\n+ \"deepseek\": \"DeepSeek\",\r\n+ \"google\": \"Google\",\r\n+ \"meta-llama\": \"Meta\",\r\n+ \"mistralai\": \"Mistral\",\r\n+ \"moonshotai\": \"MoonshotAI\",\r\n+ \"openai\": \"OpenAI\",\r\n+ \"qwen\": \"Qwen\",\r\n+ \"x-ai\": \"xAI\",\r\n+ }\r\n+ if provider_key in provider_map:\r\n+ return provider_map[provider_key]\r\n+\r\n+ fallback = provider_id.replace(\"-\", \" \").replace(\"_\", \" \").strip()\r\n+ return fallback.title() if fallback else \"OpenRouter\"\r\n+\r\n+ if name.startswith(\"gemini-\"):\r\n return \"Google\"\r\n- elif \"nova\" in model_name.lower():\r\n+ if \"nova\" in name.lower():\r\n return \"Amazon\"\r\n- elif \"claude\" in model_name.lower():\r\n+ if \"claude\" in name.lower():\r\n return \"Anthropic\"\r\n- elif \"gpt\" in model_name.lower():\r\n+ if \"gpt\" in name.lower():\r\n return \"OpenAI\"\r\n- elif model_name.startswith(\"o\"):\r\n+ if name.startswith(\"o\"):\r\n return \"OpenAI\"\r\n- else:\r\n- return \"Other\"\r\n+ return \"Other\"\r\n \r\n df[\"provider\"] = df[\"Model\"].apply(extract_provider)\r\n \r\ndiff --git a/agents_mcp_usage/evaluations/mermaid_evals/scripts/verify_retry_orchestration.py b/agents_mcp_usage/evaluations/mermaid_evals/scripts/verify_retry_orchestration.py\r\nnew file mode 100644\r\nindex 0000000..716184e\r\n--- /dev/null\r\n+++ b/agents_mcp_usage/evaluations/mermaid_evals/scripts/verify_retry_orchestration.py\r\n@@ -0,0 +1,126 @@\r\n+#!/usr/bin/env python3\r\n+\"\"\"\r\n+Offline verification for run-level retry + backoff orchestration.\r\n+\r\n+This script uses a stub Dataset to simulate:\r\n+- evaluation timeouts (retryable at run-level)\r\n+- report-based retry triggers (e.g. http_error_429, error_ExceptionGroup)\r\n+- non-retry conditions (usage_limit_exceeded, agent_timeout)\r\n+- fatal conditions (Mermaid validator unavailable)\r\n+\r\n+It patches delays to zero so it runs quickly and deterministically.\r\n+\"\"\"\r\n+\r\n+from __future__ import annotations\r\n+\r\n+import asyncio\r\n+from dataclasses import dataclass, field\r\n+from typing import Any, List, Sequence\r\n+\r\n+import agents_mcp_usage.evaluations.mermaid_evals.run_multi_evals as run_multi_evals\r\n+from agents_mcp_usage.evaluations.mermaid_evals.evals_pydantic_mcp import (\r\n+ MermaidValidatorUnavailableError,\r\n+)\r\n+from agents_mcp_usage.evaluations.mermaid_evals.run_multi_evals import MultiModelEvaluator\r\n+\r\n+\r\n+@dataclass(slots=True)\r\n+class StubOutput:\r\n+ failure_reason: str = \"\"\r\n+ fixed_diagram: str = \"\"\r\n+ metrics: dict[str, Any] = field(default_factory=dict)\r\n+ tools_used: list[str] = field(default_factory=list)\r\n+\r\n+\r\n+@dataclass(slots=True)\r\n+class StubCase:\r\n+ name: str\r\n+ task_duration: float = 0.0\r\n+ output: StubOutput = field(default_factory=StubOutput)\r\n+ scores: dict[str, Any] = field(default_factory=dict)\r\n+\r\n+\r\n+@dataclass(slots=True)\r\n+class StubReport:\r\n+ cases: list[StubCase]\r\n+\r\n+\r\n+class StubDataset:\r\n+ \"\"\"A minimal stub that matches the Dataset.evaluate() shape we need.\"\"\"\r\n+\r\n+ def __init__(self, sequence: Sequence[object]) -> None:\r\n+ self.sequence = list(sequence)\r\n+ self.calls = 0\r\n+\r\n+ async def evaluate(self, *_args: Any, **_kwargs: Any) -> object:\r\n+ item = self.sequence[self.calls]\r\n+ self.calls += 1\r\n+ if isinstance(item, BaseException):\r\n+ raise item\r\n+ return item\r\n+\r\n+\r\n+async def _no_sleep(_delay: float) -> None:\r\n+ return None\r\n+\r\n+\r\n+def _make_report(failure_reasons: List[str]) -> StubReport:\r\n+ cases = [\r\n+ StubCase(name=f\"case_{idx}\", output=StubOutput(failure_reason=reason))\r\n+ for idx, reason in enumerate(failure_reasons)\r\n+ ]\r\n+ return StubReport(cases=cases)\r\n+\r\n+\r\n+async def main() -> None:\r\n+ # Patch delay + sleep so retries are fast in this script\r\n+ run_multi_evals.compute_exponential_backoff_delay = lambda *_a, **_k: 0.0 # type: ignore[assignment]\r\n+ run_multi_evals.asyncio.sleep = _no_sleep # type: ignore[assignment]\r\n+\r\n+ model = \"test-model\"\r\n+\r\n+ # 1) Evaluation timeout should retry up to MAX_RETRY_ATTEMPTS, then fail.\r\n+ evaluator = MultiModelEvaluator(models=[model], judge_model=\"judge\", output_dir=\"./mermaid_eval_results\")\r\n+ dataset = StubDataset([asyncio.TimeoutError()] * run_multi_evals.MAX_RETRY_ATTEMPTS)\r\n+ result = await evaluator.run_single_evaluation(model, 0, dataset, timeout=0.01)\r\n+ assert result is None\r\n+ assert evaluator.results[model].failed_runs[0][\"attempts\"] == run_multi_evals.MAX_RETRY_ATTEMPTS\r\n+\r\n+ # 2) Retryable report failure_reason should trigger a retry, then succeed.\r\n+ evaluator = MultiModelEvaluator(models=[model], judge_model=\"judge\", output_dir=\"./mermaid_eval_results\")\r\n+ dataset = StubDataset([_make_report([\"http_error_429\"]), _make_report([\"\"])])\r\n+ result = await evaluator.run_single_evaluation(model, 0, dataset, timeout=1)\r\n+ assert result is not None\r\n+ assert result.attempts == 2\r\n+ assert dataset.calls == 2\r\n+\r\n+ # 3) usage_limit_exceeded should NOT trigger a retry.\r\n+ evaluator = MultiModelEvaluator(models=[model], judge_model=\"judge\", output_dir=\"./mermaid_eval_results\")\r\n+ dataset = StubDataset([_make_report([\"usage_limit_exceeded\"]), _make_report([\"\"])])\r\n+ result = await evaluator.run_single_evaluation(model, 0, dataset, timeout=1)\r\n+ assert result is not None\r\n+ assert result.attempts == 1\r\n+ assert dataset.calls == 1\r\n+\r\n+ # 4) agent_timeout should NOT trigger a retry.\r\n+ evaluator = MultiModelEvaluator(models=[model], judge_model=\"judge\", output_dir=\"./mermaid_eval_results\")\r\n+ dataset = StubDataset([_make_report([\"agent_timeout\"]), _make_report([\"\"])])\r\n+ result = await evaluator.run_single_evaluation(model, 0, dataset, timeout=1)\r\n+ assert result is not None\r\n+ assert result.attempts == 1\r\n+ assert dataset.calls == 1\r\n+\r\n+ # 5) Mermaid validator unavailability should fail-fast (raise).\r\n+ evaluator = MultiModelEvaluator(models=[model], judge_model=\"judge\", output_dir=\"./mermaid_eval_results\")\r\n+ dataset = StubDataset([MermaidValidatorUnavailableError(\"validator down\")])\r\n+ try:\r\n+ await evaluator.run_single_evaluation(model, 0, dataset, timeout=1)\r\n+ raise AssertionError(\"Expected MermaidValidatorUnavailableError\")\r\n+ except MermaidValidatorUnavailableError:\r\n+ pass\r\n+\r\n+ print(\"verify_retry_orchestration.py: OK\")\r\n+\r\n+\r\n+if __name__ == \"__main__\":\r\n+ asyncio.run(main())\r\n\\ No newline at end of file\r\ndiff --git a/agents_mcp_usage/factory/__init__.py b/agents_mcp_usage/factory/__init__.py\r\nnew file mode 100644\r\nindex 0000000..132677e\r\n--- /dev/null\r\n+++ b/agents_mcp_usage/factory/__init__.py\r\n@@ -0,0 +1 @@\r\n+\"\"\"Utils module for agents MCP usage.\"\"\"\r\ndiff --git a/mcp_servers/mermaid_validator.py b/mcp_servers/mermaid_validator.py\r\nindex 2a9a9fc..665f85c 100644\r\n--- a/mcp_servers/mermaid_validator.py\r\n+++ b/mcp_servers/mermaid_validator.py\r\n@@ -66,7 +66,7 @@ async def validate_mermaid_diagram(diagram_text: str) -> MermaidValidationResult\r\n [\r\n \"npx\",\r\n \"-y\",\r\n- \"@mermaid-js/mermaid-cli\",\r\n+ \"@mermaid-js/mermaid-cli@11.4.2\",\r\n \"-i\",\r\n temp_file_path,\r\n \"-o\",\r\n","/bin/bash -lc 'git diff 697c3fc..HEAD -- agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py agents_mcp_usage/evaluations/mermaid_evals/merbench_ui.py agents_mcp_usage/evaluations/mermaid_evals/scripts/preprocess_merbench_data.py agents_mcp_usage/evaluations/mermaid_evals/scripts/merge_benchmark_results.py agents_mcp_usage/evaluations/mermaid_evals/scripts/verify_retry_orchestration.py agents_mcp_usage/factory/__init__.py mcp_servers/mermaid_validator.py'",[1625],"76260",{"_733":1626,"_90":1590},"git diff 697c3fc..HEAD -- agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py agents_mcp_usage/evaluations/mermaid_evals/merbench_ui.py agents_mcp_usage/evaluations/mermaid_evals/scripts/preprocess_merbench_data.py agents_mcp_usage/evaluations/mermaid_evals/scripts/merge_benchmark_results.py agents_mcp_usage/evaluations/mermaid_evals/scripts/verify_retry_orchestration.py agents_mcp_usage/factory/__init__.py mcp_servers/mermaid_validator.py",{"_670":1628,"_662":663,"_672":81},{"_731":-5,"_733":1622,"_735":1629,"_737":738,"_739":-5,"_741":-5,"_41":1616,"_742":1624,"_744":745,"_667":755,"_90":746},[1630],{"_733":1626,"_90":1590},{"_670":1632,"_662":663,"_672":81},{"_759":1633,"_722":1616,"_41":-5,"_297":761,"_762":-5,"_90":763},"{\"cmd\":\"git diff 697c3fc..HEAD -- agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py agents_mcp_usage/evaluations/mermaid_evals/merbench_ui.py agents_mcp_usage/evaluations/mermaid_evals/scripts/preprocess_merbench_data.py agents_mcp_usage/evaluations/mermaid_evals/scripts/merge_benchmark_results.py agents_mcp_usage/evaluations/mermaid_evals/scripts/verify_retry_orchestration.py agents_mcp_usage/factory/__init__.py mcp_servers/mermaid_validator.py\"}",{"_670":1635,"_662":663,"_672":81},{"_673":-5,"_691":1636,"_41":1637,"_694":1638,"_90":696},"gAAAAABpaAifjvYtgU4vhAPTz6-A0Yb8oDQ3B3NeBflvLzgt2Hf83TuJiLV_D7OnPmtYBI0XPIpfN8oA6IHlrHjkcHU63_hzmHcGXteaRR3zdA0M6wQkk4V0Vg8B4EwSUJPvTsQdRfGdJ20Ic_hOdTiXE5SGevu3zkzkolcCmGO2YiseHnOmckviplZfGSF3X_NYV6V0NzxNAFkPkz9vnHA9F-bjLEbT6ubbPPzC9T1DsPlEaIG6DijRVjYYVmG5DxoN2_XB_8_VsRDSFJEMfjGulxZmt8bDl44Lw63jNODuq6g5uFyZ1e1ziyQKYPAF_k2NV11B-pB_urG9XFZYw2RqP5FQUlgvlMwGWDjjF6JLLSuYlLNIf1o4m0764-aUSME71vRyAgAtQ7BKxm3ZS1218USYMPOI0W1VEIZyGCXstaqS3KDUvrw13pOQXG-VrtHO2Iadq_lybhfVK8sfX4P9ECsk3uS25RqgSpgr6x_EZW_GNcynqmYunyF8tFpyi2BgcImDr8Vrf9kkuMK5ERGHc1tB3_fObub0zgJpiaZhwJWHfUxhDrypcVHQ_5kVm05pgkWCLtSt-5BpZEXpkfrBhLc1Bw7ALI1dt-sfYMG0kaimNIcj_qvFSP4sZM4-P2uvPYYrGtX1J8IOrd1cN26Xhy7cDuSJgn7mgAebgPqKqnTCYdR5LpRq3DHTcud87tV908yyNUaPySNWmlsfIkk7EygBtPSTPty8PmWbTFu_t5LThpADit7SDeTpr4QyT4pC2NEGT3Nm0m1f1SYHd3K9VZ22hUQKuNZjH2WgFZRJf4XDRCZ4xnSrhXkddI2PTl0DPcWn8G6HCpydSebMW0o7ODfkYBIRSgHx8xN-7uCcV_AcVRWo_qlBJLl3fq4at_58LurIdEKmQOZl8wgGVucuIFAaD5kFIWgX453-IYceXQX4YPTsgVBiWDD93MgAdj-NmAQ2CV0L3CTXThncVWgf-RJnTRlCQrlpkwdUlCv7sUI4CJ-cLhwpUNxz8aUlfB4ZvF7TYRqgQzMe2gzrzDD-E8KomQHMO0jXTDbtPgHINoDUBZINhootgq78Z1MY1wSqKLgc11dv--XNFRKFcCrea-rb2ww99TWnMxcQH7UAy9MFUgWBQYFaBDHFHlCg2HfPdVITqRJ3gb7emIEGeAND7fzKkZ2ZSLd90K6eMtN_K29B-UGM4HIEbShTjqkEJErkahbaSeL-zwUvc8Nm6fJ3KvVNGlq1sjC6V7q2_SW69Y-6wqg90I3z03McP3wW42DyyDXjmxbwU_qnxGLQNgnjFVWxGTSaqxQ22tbZ_YGP5AuFq318AMMBXGNB4E4g2n4LIqL1t2x6eAnWYU6xrGGwPDPKitv_lJrMPKhhTIFMpOuBV2fUDwaJCA47iZi1cxzG5SkidYNrKf5F1CXbqMqED2QXY8ubk5oU0LeMZTDVlA3IaCPjdfnuV3erVjp7OTskpgDk0vJyJUjXx1uaw6h0cefHbWhVcLIWzjGp_oIic82_Od7LDo6E_TtMoFBAM33gTgumicGFY4MYzzhlDu792ZABoa3OV1nJGjvHs2qQWoV7jkp60CUPxCdWuSBu7C0t-nsKdL4E0bbnmt9kp_S4bqnTeIiluPX31htnmI6ZrBgTS7ISLD4BGYn3KHTi7BEtjDP_TPNBJxfC3se3z6UqijZ_WsWFEjUvxxJGPrWbZr6Lxhe7wnCEIpmKWQwQvOjrv71rsV2-T2HfWCEyr4rtOHiTQ9WUkCLjRcOF2fBBdxuwsE0dOqWHnc-DuYvYLdJLknjfnpaHxPDmxW9VVyihzrn5XwFyYEvjEYJf80ignO443FLs6bGOUtdgMKfxnyUGxkSutIbT4X6Aqc14CjWdO-uLzZA9gg==","legacy_responseItem_reasoning_14",[1639],{"_45":1640,"_90":700},"**Planning PR summary extraction**\n\nI'm identifying key changes like the new model factory, pricing updates, OpenRouter integration, debug traces, and OpenAI response normalization to include in the PR description, focusing on eval scripts and usage; next, I'll review diffs for evaluation scripts to confirm details and verify impact on usage and outputs.",{"_670":1642,"_662":663,"_672":81},{"_673":1643,"_41":1644,"_694":1645,"_90":696},[1640],"legacy_reasoning_13",[],{"_670":1647,"_662":663,"_672":81},{"_673":1648,"_41":1644,"_694":1649,"_90":696},[],[],{"_670":1651,"_662":663,"_672":81},{"_722":1652,"_724":1653,"_90":726},"call_entS7M8TVxvntk8XJ7vKkZO2",{"_408":1654,"_728":-5},"Chunk ID: 331467\nWall time: 0.0516 seconds\nProcess exited with code 0\nOriginal token count: 12555\nOutput:\nTotal output lines: 1515\n\ndiff --git a/README.md b/README.md\r\nindex 25fe2ed..38ab4d5 100644\r\n--- a/README.md\r\n+++ b/README.md\r\n@@ -63,6 +63,7 @@ This project aims to teach:\r\n - `langgraph_mcp.py` - Example of using MCP with LangGraph\r\n - `oai-agent_mcp.py` - Example of using MCP with OpenAI Agents\r\n - `pydantic_mcp.py` - Example of using MCP with Pydantic-AI\r\n+ - `pydantic_mcp_factory.py` - Example demonstrating the model factory pattern for multi-provider support\r\n \r\n \r\n - **[agents_mcp_usage/multi_mcp/](agents_mcp_usage/multi_mcp/)** - Advanced multi-MCP server integration examples\r\ndiff --git a/agents_mcp_usage/basic_mcp/README.md b/agents_mcp_usage/basic_mcp/README.md\r\nindex 73f1597..a38f18c 100644\r\n--- a/agents_mcp_usage/basic_mcp/README.md\r\n+++ b/agents_mcp_usage/basic_mcp/README.md\r\n@@ -225,6 +225,33 @@ Key features:\r\n - Employs context manager for server lifecycle management\r\n - Includes comprehensive instrumentation for both MCP and Pydantic-AI\r\n \r\n+### Pydantic-AI with Model Factory Pattern\r\n+\r\n+**File:** `pydantic_mcp_factory.py`\r\n+\r\n+This example demonstrates how to use the model factory pattern to create agents with different providers while maintaining the same MCP functionality.\r\n+\r\n+```bash\r\n+# Use default Gemini model\r\n+uv run agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py\r\n+\r\n+# Use DeepSeek model\r\n+uv run agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py --model deepseek\r\n+\r\n+# Use OpenAI model with explicit provider\r\n+uv run agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py --model \"openai:gpt-4\"\r\n+\r\n+# List available model aliases\r\n+uv run agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py --list-models\r\n+```\r\n+\r\n+Key features:\r\n+- Demonstrates the model factory pattern for multi-provider support\r\n+- Supports 13+ providers including Google, OpenAI, Anthropic, AWS Bedrock, DeepSeek, OpenRouter, GitHub, Grok, Perplexity, and local Ollama models\r\n+- Command-line interface for easy model switching\r\n+- Shows how to handle provider-specific configurations\r\n+- Maintains the same MCP server functionality across all providers\r\n+\r\n \r\n ## Understanding the Examples\r\n \r\n@@ -245,4 +272,4 @@ All examples connect to the same MCP server defined in `mcp_servers/example_serv\r\n - A time tool (`get_current_time()`) \r\n - A dynamic greeting resource (`greeting://{name}`)\r\n \r\n-You can modify the MCP server to add your own tools and resources for experimentation. \r\n\\ No newline at end of file\r\n+You can modify the MCP server to add your own tools and resources for experimentation.\r\ndiff --git a/agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py b/agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py\r\nnew file mode 100644\r\nindex 0000000..c4184b5\r\n--- /dev/null\r\n+++ b/agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py\r\n@@ -0,0 +1,216 @@\r\n+\"\"\"\r\n+Pydantic MCP Example using Model Factory Pattern\r\n+\r\n+This example demonstrates how to use the model factory to create agents\r\n+with different providers while maintaining the same MCP server setup.\r\n+\r\n+Supported model formats:\r\n+- Standard: \"gemini-2.5-pro\", \"gpt-4\", \"claude-3-opus\"\r\n+- Provider-prefixed: \"openai:gpt-4\", \"deepseek:deepseek-chat\", \"bedrock:us.amazon.nova-pro-v1:0\"\r\n+- Local models: \"ollama:llama3.2\", \"ollama:qwen2.5-coder:7b\"\r\n+\"\"\"\r\n+\r\n+import asyncio\r\n+import argparse\r\n+from typing import Optional\r\n+\r\n+import logfire\r\n+from dotenv import load_dotenv\r\n+from pydantic_ai.mcp import MCPServerStdio\r\n+\r\n+from agents_mcp_usage.utils import get_mcp_server_path\r\n+from agents_mcp_usage.factory.model_factory import create_agent\r\n+\r\n+load_dotenv()\r\n+\r\n+# Configure logging to logfire if LOGFIRE_TOKEN is set in environment\r\n+logfire.configure(send_to_logfire=\"if-token-present\", service_name=\"pydantic-basic-mcp-factory\")\r\n+logfire.instrument_mcp()\r\n+logfire.instrument_pydantic_ai()\r\n+\r\n+# Preset model aliases demonstrating different provider formats\r\n+MODEL_ALIASES = {\r\n+ # Standard providers (auto-detected)\r\n+ \"gemini\": \"gemini-2.5-pro-preview-06-05\",\r\n+ \"gemini-flash\": \"gemini-2.5-flash\",\r\n+ \"gpt4\": \"gpt-4\",\r\n+ \"claude\": \"claude-3-opus-20240229\",\r\n+ \r\n+ # Explicit provider prefix\r\n+ \"openai-o4\": \"openai:o4-mini\",\r\n+ \"deepseek\": \"deepseek:deepseek-chat\",\r\n+ \"deepseek-reasoner\": \"deepseek:deepseek-reasoner\",\r\n+ \r\n+ # AWS Bedrock models\r\n+ \"bedrock-nova\": \"bedrock:us.amazon.nova-pro-v1:0\",\r\n+ \"bedrock-claude\": \"bedrock:us.anthropic.claude-3-5-sonnet-20240620-v1:0\",\r\n+ \r\n+ # GitHub Models (free tier)\r\n+ \"github-grok\": \"github:xai/grok-3-mini\",\r\n+ \r\n+ # Local models via Ollama\r\n+ \"ollama-llama\": \"ollama:llama3.2\",\r\n+ \"ollama-qwen\": \"ollama:qwen2.5-coder:7b\",\r\n+ \r\n+ # Other providers\r\n+ \"perplexity\": \"perplexity:sonar-pro\",\r\n+ \"grok\": \"grok:grok-2-1212\",\r\n+}\r\n+\r\n+\r\n+def get_mcp_server() -> MCPServerStdio:\r\n+ \"\"\"Get the MCP server configuration.\"\"\"\r\n+ return MCPServerStdio(\r\n+ command=\"uv\",\r\n+ args=[\r\n+ \"run\",\r\n+ str(get_mcp_server_path(\"example_server.py\")),\r\n+ \"stdio\",\r\n+ ],\r\n+ )\r\n+\r\n+\r\n+async def main(\r\n+ model: str = \"gemini-2.5-pro-preview-06-05\",\r\n+ query: str = \"Greet Andrew and give him the current time\",\r\n+ provider_kwargs: Optional[dict] = None\r\n+) -> None:\r\n+ \"\"\"Runs the Pydantic agent with a given query using the model factory.\r\n+\r\n+ This function demonstrates how to use the model factory to create agents\r\n+ with different providers while maintaining the same MCP functionality.\r\n+\r\n+ Args:\r\n+ model: The model identifier (e.g., \"gemini-2.5-pro\", \"deepseek:deepseek-chat\")\r\n+ query: The query to run the agent with\r\n+ provider_kwargs: Optional provider-specific configuration (e.g., for Azure)\r\n+ \"\"\"\r\n+ # Create MCP server\r\n+ server = get_mcp_server()\r\n+ \r\n+ # Create agent using the factory\r\n+ print(f\"Creating agent with model: {model}\")\r\n+ agent = create_agent(\r\n+ model=model,\r\n+ mcp_servers=[server],\r\n+ provider_kwargs=provider_kwargs\r\n+ )\r\n+ \r\n+ # Run the agent\r\n+ try:\r\n+ async with agent.run_mcp_servers():\r\n+ result = await agent.run(query)\r\n+ print(f\"\\nResult from {model}:\")\r\n+ print(result.output)\r\n+ \r\n+ # Print usage information if available\r\n+ usage = result.usage()\r\n+ if usage:\r\n+ print(f\"\\nToken usage: {usage.total_tokens} total \"\r\n+ f\"({usage.request_tokens} in, {usage.response_tokens} out)\")\r\n+ \r\n+ except Exception as e:\r\n+ print(f\"Error running agent: {e}\")\r\n+ print(\"\\nMake sure you have the required API keys set:\")\r\n+ print(\"- GEMINI_API_KEY for Gemini models\")\r\n+ print(\"- OPENAI_API_KEY for OpenAI models\")\r\n+ print(\"- DEEPSEEK_API_KEY for DeepSeek models\")\r\n+ print(\"- AWS credentials for Bedrock models\")\r\n+ print(\"- Other provider-specific keys as needed\")\r\n+\r\n+\r\n+def parse_arguments():\r\n+ \"\"\"Parse command-line arguments.\"\"\"\r\n+ parser = argparse.ArgumentParser(\r\n+ description=\"Run Pydantic MCP agent with different model providers\",\r\n+ formatter_class=argparse.RawDescriptionHelpFormatter,\r\n+ epilog=\"\"\"\r\n+Examples:\r\n+ # Use default Gemini model\r\n+ python pydantic_mcp_factory.py\r\n+ \r\n+ # Use a specific model by alias\r\n+ python pydantic_mcp_factory.py --model deepseek\r\n+ \r\n+ # Use a model by full name\r\n+ python pydantic_mcp_factory.py --model \"openai:gpt-4\"\r\n+ \r\n+ # Use a custom query\r\n+ python pydantic_mcp_factory.py --query \"What's the weather like?\"\r\n+ \r\n+ # List available model aliases\r\n+ python pydantic_mcp_factory.py --list-models\r\n+ \"\"\"\r\n+ )\r\n+ \r\n+ parser.add_argument(\r\n+ \"--model\", \"-m\",\r\n+ default=\"gemini\",\r\n+ help=\"Model to use (alias or full model string)\"\r\n+ )\r\n+ \r\n+ parser.add_argument(\r\n+ \"--query\", \"-q\",\r\n+ default=\"Greet Andrew and give him the current time\",\r\n+ help=\"Query to send to the agent\"\r\n+ )\r\n+ \r\n+ parser.add_argument(\r\n+ \"--list-models\", \"-l\",\r\n+ action=\"store_true\",\r\n+ help=\"List available model aliases and exit\"\r\n+ )\r\n+ \r\n+ # Provider-specific options\r\n+ parser.add_argument(\r\n+ \"--azure-endpoint\",\r\n+ help=\"Azure endpoint for Azure AI Foundry models\"\r\n+ )\r\n+ \r\n+ parser.add_argument(\r\n+ \"--azure-api-version\",\r\n+ help=\"Azure API version for Azure AI Foundry models\"\r\n+ )\r\n+ \r\n+ parser.add_argument(\r\n+ \"--ollama-base-url\",\r\n+ default=\"http://localhost:11434/v1\",\r\n+ help=\"Base URL for Ollama server\"\r\n+ )\r\n+ \r\n+ return parser.parse_args()\r\n+\r\n+\r\n+if __name__ == \"__main__\":\r\n+ args = parse_arguments()\r\n+ \r\n+ # List models and exit if requested\r\n+ if args.list_models:\r\n+ print(\"Available model aliases:\")\r\n+ print(\"-\" * 50)\r\n+ for alias, model_string in MODEL_ALIASES.items():\r\n+ print(f\"{alias:<20} -> {model_string}\")\r\n+ print(\"\\nYou can also use any model string directly, e.g.:\")\r\n+ print(\" openrouter:google/gemini-2.5-pro-preview\")\r\n+ print(\" fireworks:accounts/fireworks/models/qwq-32b\")\r\n+ print(\" together:meta-llama/Llama-3.3-70B-Instruct-Turbo-Free\")\r\n+ exit(0)\r\n+ \r\n+ # Resolve model alias if applicable\r\n+ model = MODEL_ALIASES.get(args.model, args.model)\r\n+ \r\n+ # Build provider kwargs if Azure options are provided\r\n+ provider_kwargs = {}\r\n+ if args.azure_endpoint:\r\n+ provider_kwargs[\"azure_endpoint\"] = args.azure_endpoint\r\n+ if args.azure_api_version:\r\n+ provider_kwargs[\"api_version\"] = args.azure_api_version\r\n+ if args.ollama_base_url and model.startswith(\"ollama:\"):\r\n+ provider_kwargs[\"base_url\"] = args.ollama_base_url\r\n+ \r\n+ # Run the main function\r\n+ asyncio.run(main(\r\n+ model=model,\r\n+ query=args.query,\r\n+ provider_kwargs=provider_kwargs if provider_kwargs else None\r\n+ ))\r\ndiff --git a/agents_mcp_usage/evaluations/mermaid_evals/README.md b/agents_mcp_usage/evaluations/mermaid_evals/README.md\r\nindex ee33e22..a5645af 100644\r\n--- a/agents_mcp_usage/evaluations/mermaid_evals/README.md\r\n+++ b/agents_mcp_usage/evaluations/mermaid_evals/README.md\r\n@@ -64,8 +64,9 @@ The evaluation system uses Pydantic-AI and requires environment variables for AP\r\n # Required for most models\r\n export GEMINI_API_KEY=\"your-key-here\"\r\n \r\n-# Optional for specific models\r\n+# Optional for specific providers/models\r\n export OPENAI_API_KEY=\"your-key-here\"\r\n+export OPENROUTER_API_KEY=\"your-key-here\" # required for `openrouter:*` models\r\n ```\r\n \r\n ## Quick Start Guide\r\n@@ -80,6 +81,12 @@ uv run agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py\r\n ```bash\r\n # Run evaluation across multiple models in parallel\r\n uv run agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py\r\n+\r\n+# Example: include an OpenRouter model (requires OPENROUTER_API_KEY)\r\n+uv run agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py \\\r\n+ --models \"openrouter:anthropic/claude-3.7-sonnet,gemini-3-flash-preview\" \\\r\n+ --runs 5 \\\r\n+ --sequential\r\n ```\r\n \r\n ### 3. View Results in Local Dashboard (Recommended)\r\n@@ -370,12 +377,71 @@ uv run agents_mcp_usage/evaluations/mermaid_evals/scripts/merge_benchmark_result\r\n \r\n ## Monitoring & Debugging\r\n \r\n+### Logfire (remote, if configured)\r\n+\r\n All evaluation runs are traced with **Logfire** for comprehensive monitoring:\r\n - Tool call traces\r\n - Retry attempts and reasons\r\n - Execution durations\r\n - Categorised failure analysis\r\n \r\n+### Local per-case debug traces (JSON)\r\n+\r\n+When diagnosing failure modes across models (e.g. tool-call loops, request/turn limits, redundant tool usage), you can enable a local debug mode that writes a detailed per-case trace JSON file for each model attempt.\r\n+\r\n+Enable traces:\r\n+\r\n+```bash\r\n+uv run agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py \\\r\n+ --runs 1 \\\r\n+ --sequential \\\r\n+ --debug-traces\r\n+```\r\n+\r\n+Optional: choose where traces are written:\r\n+\r\n+```bash\r\n+uv run agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py \\\r\n+ --runs 1 \\\r\n+ --sequential \\\r\n+ --debug-traces \\\r\n+ --trace-dir ./mermaid_eval_results/debug_traces\r\n+```\r\n+\r\n+By default, the trace directory is:\r\n+\r\n+- `/debug_traces/` (where `` defaults to `./mermaid_eval_results`)\r\n+\r\n+#### Output layout\r\n+\r\n+Traces are stored under a per-model subdirectory:\r\n+\r\n+```text\r\n+/\r\n+ /\r\n+ model=__run###__attempt###__case=.json\r\n+```\r\n+\r\n+> `run###` is the planned run number (1-based), `attempt###` is the runner-level retry attempt (1-based), and `case_slug` is derived from the case name (or a hash of the input if absent).\r\n+\r\n+#### What gets captured\r\n+\r\n+Each trace JSON file includes (at minimum):\r\n+\r\n+- input: invalid diagram + hash + case name\r\n+- prompt query sent to the agent\r\n+- full message history (including tool call args and tool return payloads)\r\n+- derived message summary:\r\n+ - request count, response count\r\n+ - tool call/return counts\r\n+ - tool name sequence (helps spot loops)\r\n+- usage limits used for the run\r\n+- usage/tokens (when available from the provider)\r\n+- output text and extracted mermaid diagram (on success)\r\n+- exception metadata and traceback (on failures)\r\n+\r\n+**Important:** traces contain full prompts and full tool return payloads. Treat them as sensitive and avoid sharing publicly.\r\n+\r\n ## Troubleshooting\r\n \r\n ### Common Issues\r\ndiff --git a/agents_mcp_usage/evaluations/mermaid_evals/costs.json b/agents_mcp_usage/evaluations/mermaid_evals/costs.json\r\nindex 98fd3b9..4dd6a6f 100644\r\n--- a/agents_mcp_usage/evaluations/mermaid_evals/costs.json\r\n+++ b/agents_mcp_usage/evaluations/mermaid_evals/costs.json\r\n@@ -1,10 +1,44 @@\r\n {\r\n- \"_comment\": \"Prices are per 1 million tokens\",\r\n+ \"_comment\": \"Prices are per 1 million tokens - Updated July 19, 2025\",\r\n \"_sources\": {\r\n \"gemini\": \"https://ai.google.dev/gemini-api/docs/pricing\",\r\n- \"openai\": \"https://openai.com/api/pricing/\"\r\n+ \"openai\": \"https://openai.com/api/pricing/\",\r\n+ \"bedrock\": \"https://aws.amazon.com/bedrock/pricing/\",\r\n+ \"deepseek\": \"https://platform.deepseek.com/api-docs/pricing\",\r\n+ \"openrouter\": \"https://openrouter.ai/models\",\r\n+ \"github\": \"https://github.com/marketplace/models\",\r\n+ \"grok\": \"https://console.x.ai/pricing\",\r\n+ \"perplexity\": \"https://www.perplexity.ai/api/pricing\",\r\n+ \"fireworks\": \"https://fireworks.ai/pricing\",\r\n+ \"together\": \"https://www.together.ai/pricing\",\r\n+ \"heroku\": \"https://www.heroku.com/ai\"\r\n },\r\n \"model_costs\": {\r\n+ \"gemini-3-pro-preview\": {\r\n+ \"friendly_name\": \"Gemini 3.0 Pro Preview (Nov)\",\r\n+ \"input\": [\r\n+ {\r\n+ \"up_to\": 200000,\r\n+ \"price\": 2.0\r\n+ },\r\n+ {\r\n+ \"up_to\": \"inf\",\r\n+ \"price\": 4.0\r\n+ }\r\n+ ],\r\n+ \"output\": {\r\n+ \"default\": [\r\n+ {\r\n+ \"up_to\": 200000,\r\n+ \"price\": 12.0\r\n+ },\r\n+ {\r\n+ \"up_to\": \"inf\",\r\n+ \"price\": 18.0\r\n+ }\r\n+ ]\r\n+ }\r\n+ },\r\n \"gemini-2.5-pro-preview-03-25\": {\r\n \"friendly_name\": \"Gemini 2.5 Pro Preview (Mar)\",\r\n \"input\": [\r\n@@ -266,12 +300,29 @@\r\n ]\r\n }\r\n },\r\n+ \"gemini-3-flash-preview\": {\r\n+ \"friendly_name\": \"Gemini 3 Flash Preview\",\r\n+ \"input\": [\r\n+ {\r\n+ \"up_to\": \"inf\",\r\n+ \"price\": 0.5\r\n+ }\r\n+ ],\r\n+ \"output\": {\r\n+ \"default\": [\r\n+ {\r\n+ \"up_to\": \"inf\",\r\n+ \"price\": 3.0\r\n+ }\r\n+ ]\r\n+ }\r\n+ },\r\n \"gemini-2.5-flash\": {\r\n \"friendly_name\": \"Gemini 2.5 Flash\",\r\n \"input\": [\r\n {\r\n \"up_to\": \"inf\",\r\n- \"price\": 0.3\r\n+ \"price\": 0.15\r\n }\r\n ],\r\n \"output\": {\r\n@@ -295,7 +346,13 @@\r\n \"default\": [\r\n {\r\n \"up_to\": \"inf\",\r\n- \"price\": 2.5\r\n+ \"price\": 0.6\r\n+ }\r\n+ ],\r\n+ \"thinking\": [\r\n+ {\r\n+ \"up_to\": \"inf\",\r\n+ \"price\": 3.5\r\n }\r\n ]\r\n }\r\n@@ -317,8 +374,8 @@\r\n ]\r\n }\r\n },\r\n- \"gemini-2.5-flash-lite\": {\r\n- \"friendly_name\": \"Gemini 2.5 Flash Lite\",\r\n+ \"gemini-2.5-flash-lite-preview-09-25\": {\r\n+ \"friendly_name\": \"Gemini 2.5 Flash Lite Preview (Sep)\",\r\n \"input\": [\r\n {\r\n \"up_to\": \"inf\",\r\n@@ -334,8 +391,8 @@\r\n ]\r\n }\r\n },\r\n- \"gemini-2.5-flash-lite-preview-09-2025\": {\r\n- \"friendly_name\": \"Gemini 2.5 Flash Lite Preview (Sept)\",\r\n+ \"gemini-2.5-flash-lite\": {\r\n+ \"friendly_name\": \"Gemini 2.5 Flash Lite\",\r\n \"input\": [\r\n {\r\n \"up_to\": \"inf\",\r\n@@ -373,14 +430,31 @@\r\n \"input\": [\r\n {\r\n \"up_to\": \"inf\",\r\n- \"price\": 10.0\r\n+ \"price\": 2.0\r\n+ }\r\n+ ],\r\n+ \"output\": {\r\n+ \"default\": [\r\n+ {\r\n+ \"up_to\": \"inf\",\r\n+ \"price\": 8.0\r\n+ }\r\n+ ]\r\n+ }\r\n+ },\r\n+ \"openai:o3-pro\": {\r\n+ \"friendly_name\": \"OpenAI o3-pro\",\r\n+ \"input\": [\r\n+ {\r\n+ \"up_to\": \"inf\",\r\n+ \"price\": 20.0\r\n }\r\n ],\r\n \"output\": {\r\n \"default\": [\r\n {\r\n \"up_to\": \"inf\",\r\n- \"price\": 40.0\r\n+ \"price\": 80.0\r\n }\r\n ]\r\n }\r\n@@ -436,6 +510,71 @@\r\n ]\r\n }\r\n },\r\n+ \"openai:gpt-5\": {\r\n+ \"friendly_name\": \"GPT-5\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 1.25}],\r\n+ \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 10.0}]}\r\n+ },\r\n+ \"openai:gpt-5-mini\": {\r\n+ \"friendly_name\": \"GPT-5 Mini\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 0.25}],\r\n+ \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 2.0}]}\r\n+ },\r\n+ \"openai:gpt-5-nano\": {\r\n+ \"friendly_name\": \"GPT-5 Nano\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 0.05}],\r\n+ \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 0.4}]}\r\n+ },\r\n+ \"openai:gpt-5-pro\": {\r\n+ \"friendly_name\": \"GPT-5 Pro\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 15.0}],\r\n+ \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 120.0}]}\r\n+ },\r\n+ \"openai:gpt-5-chat-latest\": {\r\n+ \"friendly_name\": \"GPT-5 Chat Latest\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 1.25}],\r\n+ \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 10.0}]}\r\n+ },\r\n+ \"openai:gpt-5-codex\": {\r\n+ \"friendly_name\": \"GPT-5 Codex\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 1.25}],\r\n+ \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 10.0}]}\r\n+ },\r\n+ \"openai:gpt-5.1\": {\r\n+ \"friendly_name\": \"GPT-5.1\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 1.25}],\r\n+ \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 10.0}]}\r\n+ },\r\n+ \"openai:gpt-5.1-codex\": {\r\n+ \"friendly_name\": \"GPT-5.1 Codex\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 1.25}],\r\n+ \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 10.0}]}\r\n+ },\r\n+ \"openai:gpt-5.1-codex-max\": {\r\n+ \"friendly_name\": \"GPT-5.1 Codex Max\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 1.25}],\r\n+ \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 10.0}]}\r\n+ },\r\n+ \"openai:gpt-5.1-codex-mini\": {\r\n+ \"friendly_name\": \"GPT-5.1 Codex Mini\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 0.25}],\r\n+ \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 2.0}]}\r\n+ },\r\n+ \"openai:gpt-5-search-api\": {\r\n+ \"friendly_name\": \"GPT-5 Search API\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 1.25}],\r\n+ \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 10.0}]}\r\n+ },\r\n+ \"openai:gpt-5.2\": {\r\n+ \"friendly_name\": \"GPT-5.2\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 1.75}],\r\n+ \"output\": {\"default\": [{\"up_to…2555 tokens truncated… }\r\n+}\r\n+```\r\n+\r\n+## Architecture\r\n+\r\n+### Components\r\n+\r\n+1. **parse_model_string()**: Extracts provider and model name from string\r\n+2. **create_model()**: Creates appropriate model instance or returns string for PydanticAI\r\n+3. **create_agent()**: Convenience function to create agents with proper model handling\r\n+4. **PROVIDER_CONFIGS**: Central configuration for all providers\r\n+\r\n+### Flow\r\n+\r\n+1. Model string is parsed to identify provider\r\n+2. If native provider (Google/OpenAI/Anthropic), return string for PydanticAI\r\n+3. If configured provider, create appropriate model instance\r\n+4. If unknown provider, return string and let PydanticAI handle it\r\n+5. Apply model profiles if specified in configuration\r\n+\r\n+## Benefits\r\n+\r\n+1. **Centralized Configuration**: All provider settings in one place\r\n+2. **Extensibility**: Easy to add new providers\r\n+3. **Consistency**: Same interface regardless of provider\r\n+4. **Flexibility**: Supports both native and custom providers\r\n+5. **Type Safety**: Leverages PydanticAI's type system\r\n+\r\n+## Example: Multi-Provider Comparison\r\n+\r\n+```python\r\n+from agents_mcp_usage.factory.model_factory import create_agent\r\n+\r\n+models = [\r\n+ \"gemini-2.5-pro\",\r\n+ \"openai:gpt-4\",\r\n+ \"deepseek:deepseek-chat\",\r\n+ \"ollama:llama3.2\"\r\n+]\r\n+\r\n+for model in models:\r\n+ agent = create_agent(\r\n+ model=model,\r\n+ mcp_servers=get_mcp_servers()\r\n+ )\r\n+ result = await agent.run(\"Solve this problem...\")\r\n+ print(f\"{model}: {result}\")\r\n+```\r\n+\r\n+## Environment Variables\r\n+\r\n+Most providers require API keys via environment variables:\r\n+\r\n+```bash\r\n+# Native providers\r\n+export GEMINI_API_KEY=\"...\"\r\n+export OPENAI_API_KEY=\"...\"\r\n+export ANTHROPIC_API_KEY=\"...\"\r\n+\r\n+ # Extended providers\r\n+ export DEEPSEEK_API_KEY=\"...\"\r\n+ export OPENROUTER_API_KEY=\"...\"\r\n+ export OPENROUTER_APP_URL=\"...\" # optional (app attribution)\r\n+ export OPENROUTER_APP_TITLE=\"...\" # optional (app attribution)\r\n+ export GITHUB_API_KEY=\"...\"\r\n+ export XAI_API_KEY=\"...\"\r\n+ export PERPLEXITY_API_KEY=\"...\"\r\n+\r\n+# AWS Bedrock\r\n+export AWS_REGION=\"us-east-1\"\r\n+export AWS_PROFILE=\"default\"\r\n+```\r\n+\r\n+## Migration Guide\r\n+\r\n+### Old Approach\r\n+```python\r\n+if model.startswith(\"bedrock:\"):\r\n+ from pydantic_ai.models.bedrock import BedrockConverseModel\r\n+ from pydantic_ai.providers.bedrock import BedrockProvider\r\n+ \r\n+ model_name = model.replace(\"bedrock:\", \"\")\r\n+ bedrock_model = BedrockConverseModel(\r\n+ model_name,\r\n+ provider=BedrockProvider(\r\n+ region_name=os.getenv(\"AWS_REGION\", \"us-east-1\"),\r\n+ profile_name=os.getenv(\"AWS_PROFILE\", \"default\")\r\n+ )\r\n+ )\r\n+ agent = Agent(bedrock_model, ...)\r\n+```\r\n+\r\n+### New Approach\r\n+```python\r\n+from agents_mcp_usage.factory.model_factory import create_agent\r\n+\r\n+agent = create_agent(\r\n+ model=\"bedrock:us.amazon.nova-pro-v1:0\",\r\n+ mcp_servers=get_mcp_servers()\r\n+)\r\n+```\r\n+\r\n+The model factory handles all the provider-specific logic internally, providing a cleaner and more maintainable approach.\r\ndiff --git a/agents_mcp_usage/factory/model_factory.py b/agents_mcp_usage/factory/model_factory.py\r\nnew file mode 100644\r\nindex 0000000..7b62fd2\r\n--- /dev/null\r\n+++ b/agents_mcp_usage/factory/model_factory.py\r\n@@ -0,0 +1,431 @@\r\n+\"\"\"\r\n+Model Factory for PydanticAI\r\n+\r\n+This module provides a centralized way to create models across different providers,\r\n+leveraging PydanticAI's built-in provider support and handling special cases.\r\n+\"\"\"\r\n+\r\n+import os\r\n+import re\r\n+from typing import Any, Dict, Optional, Union\r\n+from pydantic_ai import Agent, InlineDefsJsonSchemaTransformer\r\n+from pydantic_ai.models import Model\r\n+from pydantic_ai.models.openai import OpenAIResponsesModel\r\n+from pydantic_ai.profiles.openai import OpenAIModelProfile\r\n+from pydantic_ai.providers.openai import OpenAIProvider\r\n+\r\n+\r\n+# Providers that PydanticAI handles natively with provider:model syntax.\r\n+#\r\n+# NOTE:\r\n+# - `openrouter:*` is intentionally **not** treated as native in this repo.\r\n+# We construct an explicit OpenRouter provider + OpenAI-compatible model so we\r\n+# can control configuration and avoid relying on implicit provider detection.\r\n+NATIVE_PROVIDERS = {\r\n+ \"deepseek\", \"github\", \"grok\", \"azure\",\r\n+ \"fireworks\", \"together\", \"heroku\"\r\n+}\r\n+\r\n+# Special cases that need custom handling\r\n+PROVIDER_CONFIGS = {\r\n+ \"bedrock\": {\r\n+ # Bedrock uses a different model class\r\n+ \"handler\": \"bedrock_handler\",\r\n+ \"env_vars\": {\r\n+ \"region\": \"AWS_REGION\",\r\n+ \"profile\": \"AWS_PROFILE\"\r\n+ }\r\n+ },\r\n+ \"ollama\": {\r\n+ # Ollama is OpenAI-compatible but needs custom base URL\r\n+ \"handler\": \"openai_compatible_handler\",\r\n+ \"base_url\": \"http://localhost:11434/v1\",\r\n+ \"api_key\": \"not-needed\",\r\n+ \"profile\": {\r\n+ \"json_schema_transformer\": InlineDefsJsonSchemaTransformer,\r\n+ \"openai_supports_strict_tool_definition\": False\r\n+ }\r\n+ },\r\n+ \"perplexity\": {\r\n+ # Perplexity is OpenAI-compatible with custom base URL\r\n+ \"handler\": \"openai_compatible_handler\",\r\n+ \"base_url\": \"https://api.perplexity.ai\",\r\n+ \"env_var\": \"PERPLEXITY_API_KEY\"\r\n+ },\r\n+ \"openrouter\": {\r\n+ # OpenRouter has first-class support in PydanticAI, but we construct the\r\n+ # provider/model explicitly so configuration is controlled by this repo.\r\n+ \"handler\": \"openrouter_handler\",\r\n+ \"env_var\": \"OPENROUTER_API_KEY\",\r\n+ \"env_vars\": {\r\n+ \"app_url\": \"OPENROUTER_APP_URL\",\r\n+ \"app_title\": \"OPENROUTER_APP_TITLE\",\r\n+ },\r\n+ }\r\n+}\r\n+\r\n+\r\n+def ensure_openai_responses_model(model_string: str) -> str:\r\n+ \"\"\"Ensure OpenAI models use the Responses API.\r\n+\r\n+ This helper normalises any OpenAI model identifier to the\r\n+ `openai-responses:` provider namespace so that PydanticAI\r\n+ will use the OpenAI Responses API rather than Chat Completions.\r\n+\r\n+ Examples:\r\n+ \"gpt-5\" -> \"openai-responses:gpt-5\"\r\n+ \"openai:gpt-5-mini\" -> \"openai-responses:gpt-5-mini\"\r\n+ \"openai-responses:gpt-5\" -> \"openai-responses:gpt-5\" (unchanged)\r\n+ \"\"\"\r\n+ # Avoid double-prefixing if already using Responses API directly\r\n+ if model_string.startswith(\"openai-responses:\"):\r\n+ return model_string\r\n+\r\n+ provider, model_name = parse_model_string(model_string)\r\n+ if provider == \"openai\":\r\n+ return f\"openai-responses:{model_name}\"\r\n+\r\n+ return model_string\r\n+\r\n+\r\n+# Accept optional reasoning hints for models that support it\r\n+REASONING_SUFFIX_RE = re.compile(r\"\\s*\\((low|medium|high|none|minimal|xhigh)\\)\\s*$\", re.IGNORECASE)\r\n+\r\n+\r\n+def extract_reasoning_effort(model_string: str) -> tuple[str, Optional[str]]:\r\n+ \"\"\"Strip trailing reasoning hint like \"(medium)\" or \"(none)\" from model string.\r\n+\r\n+ Returns the base model string and the effort level (lowercased) if present.\r\n+ \"\"\"\r\n+\r\n+ match = REASONING_SUFFIX_RE.search(model_string)\r\n+ if not match:\r\n+ return model_string.strip(), None\r\n+\r\n+ effort = match.group(1).lower()\r\n+ base = REASONING_SUFFIX_RE.sub(\"\", model_string).strip()\r\n+ return base, effort\r\n+\r\n+\r\n+def parse_model_string(model_string: str) -> tuple[Optional[str], str]:\r\n+ \"\"\"\r\n+ Parse a model string to extract provider and model name.\r\n+ \r\n+ Args:\r\n+ model_string: Model identifier (e.g., \"openai:gpt-4\", \"deepseek:deepseek-chat\", \"gemini-2.5-pro\")\r\n+ \r\n+ Returns:\r\n+ Tuple of (provider, model_name)\r\n+ \"\"\"\r\n+ if \":\" in model_string:\r\n+ provider, model_name = model_string.split(\":\", 1)\r\n+ return provider, model_name\r\n+ \r\n+ # Check for known prefixes without colon\r\n+ if model_string.startswith(\"gemini-\"):\r\n+ return \"google\", model_string\r\n+ elif model_string.startswith(\"gpt-\") or model_string.startswith(\"o1-\") or model_string.startswith(\"o3-\") or model_string.startswith(\"o4-\"):\r\n+ return \"openai\", model_string\r\n+ elif model_string.startswith(\"claude-\"):\r\n+ return \"anthropic\", model_string\r\n+ \r\n+ # Default to no specific provider\r\n+ return None, model_string\r\n+\r\n+\r\n+def handle_bedrock_model(\r\n+ model_name: str,\r\n+ provider_kwargs: Optional[Dict[str, Any]] = None\r\n+) -> Model:\r\n+ \"\"\"Handle AWS Bedrock models which require special treatment.\"\"\"\r\n+ from pydantic_ai.models.bedrock import BedrockConverseModel\r\n+ from pydantic_ai.providers.bedrock import BedrockProvider\r\n+ \r\n+ config = PROVIDER_CONFIGS[\"bedrock\"]\r\n+ provider_kwargs = provider_kwargs or {}\r\n+ \r\n+ region = provider_kwargs.get(\"region_name\") or os.getenv(config[\"env_vars\"][\"region\"], \"us-east-1\")\r\n+ profile = provider_kwargs.get(\"profile_name\") or os.getenv(config[\"env_vars\"][\"profile\"], \"default\")\r\n+ \r\n+ provider_instance = BedrockProvider(\r\n+ region_name=region,\r\n+ profile_name=profile\r\n+ )\r\n+ \r\n+ return BedrockConverseModel(\r\n+ model_name,\r\n+ provider=provider_instance\r\n+ )\r\n+\r\n+\r\n+def handle_openai_compatible(\r\n+ provider: str,\r\n+ model_name: str,\r\n+ provider_kwargs: Optional[Dict[str, Any]] = None\r\n+) -> Model:\r\n+ \"\"\"Handle OpenAI-compatible providers that need custom configuration.\"\"\"\r\n+ config = PROVIDER_CONFIGS[provider]\r\n+ provider_kwargs = provider_kwargs or {}\r\n+ \r\n+ # Build OpenAI provider kwargs\r\n+ openai_kwargs = {}\r\n+ \r\n+ # Set base URL\r\n+ if \"base_url\" in config:\r\n+ openai_kwargs[\"base_url\"] = provider_kwargs.get(\"base_url\", config[\"base_url\"])\r\n+ \r\n+ # Handle API key\r\n+ if \"api_key\" in config:\r\n+ openai_kwargs[\"api_key\"] = config[\"api_key\"]\r\n+ elif \"env_var\" in config:\r\n+ api_key = provider_kwargs.get(\"api_key\") or os.getenv(config[\"env_var\"])\r\n+ if api_key:\r\n+ openai_kwargs[\"api_key\"] = api_key\r\n+ \r\n+ # Create OpenAI provider\r\n+ provider_instance = OpenAIProvider(**openai_kwargs)\r\n+ \r\n+ # Create model with profile if specified\r\n+ if config.get(\"profile\"):\r\n+ profile = OpenAIModelProfile(**config[\"profile\"])\r\n+ return OpenAIResponsesModel(\r\n+ model_name,\r\n+ provider=provider_instance,\r\n+ profile=profile,\r\n+ )\r\n+ else:\r\n+ return OpenAIResponsesModel(\r\n+ model_name,\r\n+ provider=provider_instance,\r\n+ )\r\n+\r\n+\r\n+def handle_openrouter_model(\r\n+ model_name: str,\r\n+ provider_kwargs: Optional[Dict[str, Any]] = None,\r\n+) -> Model:\r\n+ \"\"\"Handle OpenRouter models via an explicit provider/model.\r\n+\r\n+ PydanticAI can accept `openrouter:` strings directly, but we build\r\n+ the provider/model explicitly so this repository owns configuration and can\r\n+ support optional app attribution.\r\n+ \"\"\"\r\n+\r\n+ # Import lazily so users without the optional dependency can still import\r\n+ # the rest of the repo.\r\n+ #\r\n+ # NOTE: In pydantic-ai-slim==1.17.x, OpenRouter uses the OpenAI-compatible\r\n+ # models (Chat Completions) with an `OpenRouterProvider`, rather than a\r\n+ # dedicated `OpenRouterModel` class.\r\n+ import json\r\n+\r\n+ from openai import AsyncOpenAI\r\n+ from openai.types import chat\r\n+ from pydantic import ValidationError\r\n+ from pydantic_ai.models.openai import OpenAIChatModel\r\n+ from pydantic_ai.providers.openrouter import OpenRouterProvider\r\n+\r\n+ class OpenRouterPatchedChatModel(OpenAIChatModel):\r\n+ \"\"\"OpenAIChatModel with OpenRouter response sanitation.\r\n+\r\n+ Some OpenRouter-backed models (notably Anthropic) occasionally return\r\n+ invalid tool-call payloads such as `function.arguments = null`. PydanticAI\r\n+ validates the OpenAI response strictly, which raises\r\n+ `UnexpectedModelBehavior` before tool execution can happen.\r\n+\r\n+ We patch the raw ChatCompletion dict prior to Pydantic validation.\r\n+ \"\"\"\r\n+\r\n+ def _process_response(self, response: chat.ChatCompletion | str): # type: ignore[override]\r\n+ if isinstance(response, chat.ChatCompletion):\r\n+ data = response.model_dump()\r\n+\r\n+ # Fix invalid tool_calls payloads (e.g. `arguments: null`)\r\n+ try:\r\n+ choices = data.get(\"choices\")\r\n+ if isinstance(choices, list):\r\n+ for choice in choices:\r\n+ if not isinstance(choice, dict):\r\n+ continue\r\n+ message = choice.get(\"message\")\r\n+ if not isinstance(message, dict):\r\n+ continue\r\n+ tool_calls = message.get(\"tool_calls\")\r\n+ if not isinstance(tool_calls, list):\r\n+ continue\r\n+ for call in tool_calls:\r\n+ if not isinstance(call, dict):\r\n+ continue\r\n+ if call.get(\"type\") != \"function\":\r\n+ continue\r\n+ fn = call.get(\"function\")\r\n+ if not isinstance(fn, dict):\r\n+ fn = {}\r\n+ call[\"function\"] = fn\r\n+ args = fn.get(\"arguments\")\r\n+ if args is None:\r\n+ fn[\"arguments\"] = \"{}\"\r\n+ elif not isinstance(args, str):\r\n+ fn[\"arguments\"] = json.dumps(args)\r\n+ except Exception:\r\n+ # Best-effort only; fall through to default handling.\r\n+ pass\r\n+\r\n+ try:\r\n+ response = chat.ChatCompletion.model_validate(data)\r\n+ except ValidationError:\r\n+ # If sanitation didn't help, let the base model raise a\r\n+ # helpful UnexpectedModelBehavior.\r\n+ pass\r\n+\r\n+ return super()._process_response(response)\r\n+\r\n+ config = PROVIDER_CONFIGS[\"openrouter\"]\r\n+ provider_kwargs = provider_kwargs or {}\r\n+\r\n+ api_key = provider_kwargs.get(\"api_key\") or os.getenv(config[\"env_var\"])\r\n+ if not api_key:\r\n+ raise ValueError(\r\n+ \"OpenRouter API key not configured. \"\r\n+ \"Set OPENROUTER_API_KEY or pass provider_kwargs={'api_key': '...'}\"\r\n+ )\r\n+\r\n+ app_url = provider_kwargs.get(\"app_url\") or os.getenv(config[\"env_vars\"][\"app_url\"])\r\n+ app_title = provider_kwargs.get(\"app_title\") or os.getenv(config[\"env_vars\"][\"app_title\"])\r\n+\r\n+ # OpenRouter app attribution uses HTTP headers:\r\n+ # - HTTP-Referer: your app URL\r\n+ # - X-Title: your app name\r\n+ # See: https://openrouter.ai/docs/app-attribution\r\n+ default_headers: dict[str, str] = {}\r\n+ if app_url:\r\n+ default_headers[\"HTTP-Referer\"] = app_url\r\n+ if app_title:\r\n+ default_headers[\"X-Title\"] = app_title\r\n+\r\n+ base_url = provider_kwargs.get(\"base_url\") or \"https://openrouter.ai/api/v1\"\r\n+ openai_client = AsyncOpenAI(\r\n+ base_url=base_url,\r\n+ api_key=api_key,\r\n+ default_headers=default_headers or None,\r\n+ )\r\n+ provider_instance = OpenRouterProvider(openai_client=openai_client)\r\n+\r\n+ return OpenRouterPatchedChatModel(model_name, provider=provider_instance)\r\n+\r\n+\r\n+def create_model(\r\n+ model_string: str,\r\n+ model_settings: Optional[Dict[str, Any]] = None,\r\n+ provider_kwargs: Optional[Dict[str, Any]] = None\r\n+) -> Union[Model, str]:\r\n+ \"\"\"\r\n+ Create a model instance from a model string.\r\n+ \r\n+ This function:\r\n+ 1. Returns the string directly for providers PydanticAI handles natively\r\n+ 2. Handles special cases like Bedrock and OpenAI-compatible providers\r\n+ 3. Falls back to returning the string for unknown providers\r\n+ \r\n+ Args:\r\n+ model_string: Model identifier\r\n+ model_settings: Optional model-specific settings\r\n+ provider_kwargs: Optional provider-specific kwargs (e.g., azure_endpoint)\r\n+ \r\n+ Returns:\r\n+ Either a Model instance or the original string for PydanticAI to handle\r\n+ \"\"\"\r\n+ # First, normalise any OpenAI models to use the Responses API\r\n+ model_string = ensure_openai_responses_model(model_string)\r\n+\r\n+ provider, model_name = parse_model_string(model_string)\r\n+ \r\n+ # No provider prefix - let PydanticAI handle it\r\n+ if provider is None:\r\n+ return model_string\r\n+ \r\n+ # Standard providers that PydanticAI auto-detects\r\n+ # Note: OpenAI models have been converted to \"openai-responses:*\" above\r\n+ if provider in [\"google\", \"anthropic\"]:\r\n+ return model_string\r\n+ \r\n+ # Providers with native PydanticAI support\r\n+ if provider in NATIVE_PROVIDERS:\r\n+ # Special validation for Azure\r\n+ if provider == \"azure\" and provider_kwargs:\r\n+ required = [\"azure_endpoint\", \"api_version\"]\r\n+ missing = [k for k in required if k not in provider_kwargs]\r\n+ if missing:\r\n+ raise ValueError(\r\n+ f\"Azure provider requires {missing} in provider_kwargs. \"\r\n+ f\"Alternatively, let PydanticAI handle it by using the string directly.\"\r\n+ )\r\n+ # Return the string - PydanticAI will handle it\r\n+ return model_string\r\n+ \r\n+ # Handle special cases\r\n+ if provider in PROVIDER_CONFIGS:\r\n+ config = PROVIDER_CONFIGS[provider]\r\n+ handler = config.get(\"handler\")\r\n+ \r\n+ if handler == \"bedrock_handler\":\r\n+ return handle_bedrock_model(model_name, provider_kwargs)\r\n+ elif handler == \"openai_compatible_handler\":\r\n+ return handle_openai_compatible(provider, model_name, provider_kwargs)\r\n+ elif handler == \"openrouter_handler\":\r\n+ return handle_openrouter_model(model_name, provider_kwargs)\r\n+ \r\n+ # Unknown provider - return string and let PydanticAI try\r\n+ return model_string\r\n+\r\n+\r\n+def create_agent(\r\n+ model: Union[str, Model],\r\n+ mcp_servers=None,\r\n+ model_settings: Optional[Dict[str, Any]] = None,\r\n+ provider_kwargs: Optional[Dict[str, Any]] = None,\r\n+ **agent_kwargs\r\n+) -> Agent:\r\n+ \"\"\"\r\n+ Create an Agent with proper model handling.\r\n+ \r\n+ This is a convenience function that:\r\n+ 1. Converts model strings to appropriate Model instances when needed\r\n+ 2. Passes through Model instances unchanged\r\n+ 3. Leverages PydanticAI's native provider support\r\n+ \r\n+ Args:\r\n+ model: Model string or Model instance\r\n+ mcp_servers: Optional MCP servers\r\n+ model_settings: Optional model settings\r\n+ provider_kwargs: Optional provider-specific kwargs\r\n+ **agent_kwargs: Additional Agent kwargs\r\n+ \r\n+ Returns:\r\n+ Configured Agent instance\r\n+ \"\"\"\r\n+ if isinstance(model, str):\r\n+ base_model, reasoning_effort = extract_reasoning_effort(model)\r\n+\r\n+ # Ensure we have a dict to attach model-level settings\r\n+ if model_settings is None:\r\n+ model_settings = {}\r\n+\r\n+ # Only apply reasoning hint to OpenAI Responses-capable models\r\n+ provider, _ = parse_model_string(base_model)\r\n+ if (\r\n+ reasoning_effort\r\n+ and isinstance(model_settings, dict)\r\n+ and provider in {\"openai\", \"openai-responses\"}\r\n+ and \"openai_reasoning_effort\" not in model_settings\r\n+ ):\r\n+ model_settings[\"openai_reasoning_effort\"] = reasoning_effort\r\n+\r\n+ model = create_model(base_model, model_settings, provider_kwargs)\r\n+\r\n+ return Agent(\r\n+ model,\r\n+ mcp_servers=mcp_servers,\r\n+ model_settings=model_settings if isinstance(model, str) else None,\r\n+ **agent_kwargs\r\n+ )\r\n",{"_670":1656,"_662":663,"_672":81},{"_731":1657,"_733":1658,"_735":1659,"_737":738,"_739":740,"_741":31,"_41":1652,"_742":1660,"_744":745,"_667":111,"_90":746},"Total output lines: 1515\n\ndiff --git a/README.md b/README.md\r\nindex 25fe2ed..38ab4d5 100644\r\n--- a/README.md\r\n+++ b/README.md\r\n@@ -63,6 +63,7 @@ This project aims to teach:\r\n - `langgraph_mcp.py` - Example of using MCP with LangGraph\r\n - `oai-agent_mcp.py` - Example of using MCP with OpenAI Agents\r\n - `pydantic_mcp.py` - Example of using MCP with Pydantic-AI\r\n+ - `pydantic_mcp_factory.py` - Example demonstrating the model factory pattern for multi-provider support\r\n \r\n \r\n - **[agents_mcp_usage/multi_mcp/](agents_mcp_usage/multi_mcp/)** - Advanced multi-MCP server integration examples\r\ndiff --git a/agents_mcp_usage/basic_mcp/README.md b/agents_mcp_usage/basic_mcp/README.md\r\nindex 73f1597..a38f18c 100644\r\n--- a/agents_mcp_usage/basic_mcp/README.md\r\n+++ b/agents_mcp_usage/basic_mcp/README.md\r\n@@ -225,6 +225,33 @@ Key features:\r\n - Employs context manager for server lifecycle management\r\n - Includes comprehensive instrumentation for both MCP and Pydantic-AI\r\n \r\n+### Pydantic-AI with Model Factory Pattern\r\n+\r\n+**File:** `pydantic_mcp_factory.py`\r\n+\r\n+This example demonstrates how to use the model factory pattern to create agents with different providers while maintaining the same MCP functionality.\r\n+\r\n+```bash\r\n+# Use default Gemini model\r\n+uv run agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py\r\n+\r\n+# Use DeepSeek model\r\n+uv run agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py --model deepseek\r\n+\r\n+# Use OpenAI model with explicit provider\r\n+uv run agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py --model \"openai:gpt-4\"\r\n+\r\n+# List available model aliases\r\n+uv run agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py --list-models\r\n+```\r\n+\r\n+Key features:\r\n+- Demonstrates the model factory pattern for multi-provider support\r\n+- Supports 13+ providers including Google, OpenAI, Anthropic, AWS Bedrock, DeepSeek, OpenRouter, GitHub, Grok, Perplexity, and local Ollama models\r\n+- Command-line interface for easy model switching\r\n+- Shows how to handle provider-specific configurations\r\n+- Maintains the same MCP server functionality across all providers\r\n+\r\n \r\n ## Understanding the Examples\r\n \r\n@@ -245,4 +272,4 @@ All examples connect to the same MCP server defined in `mcp_servers/example_serv\r\n - A time tool (`get_current_time()`) \r\n - A dynamic greeting resource (`greeting://{name}`)\r\n \r\n-You can modify the MCP server to add your own tools and resources for experimentation. \r\n\\ No newline at end of file\r\n+You can modify the MCP server to add your own tools and resources for experimentation.\r\ndiff --git a/agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py b/agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py\r\nnew file mode 100644\r\nindex 0000000..c4184b5\r\n--- /dev/null\r\n+++ b/agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py\r\n@@ -0,0 +1,216 @@\r\n+\"\"\"\r\n+Pydantic MCP Example using Model Factory Pattern\r\n+\r\n+This example demonstrates how to use the model factory to create agents\r\n+with different providers while maintaining the same MCP server setup.\r\n+\r\n+Supported model formats:\r\n+- Standard: \"gemini-2.5-pro\", \"gpt-4\", \"claude-3-opus\"\r\n+- Provider-prefixed: \"openai:gpt-4\", \"deepseek:deepseek-chat\", \"bedrock:us.amazon.nova-pro-v1:0\"\r\n+- Local models: \"ollama:llama3.2\", \"ollama:qwen2.5-coder:7b\"\r\n+\"\"\"\r\n+\r\n+import asyncio\r\n+import argparse\r\n+from typing import Optional\r\n+\r\n+import logfire\r\n+from dotenv import load_dotenv\r\n+from pydantic_ai.mcp import MCPServerStdio\r\n+\r\n+from agents_mcp_usage.utils import get_mcp_server_path\r\n+from agents_mcp_usage.factory.model_factory import create_agent\r\n+\r\n+load_dotenv()\r\n+\r\n+# Configure logging to logfire if LOGFIRE_TOKEN is set in environment\r\n+logfire.configure(send_to_logfire=\"if-token-present\", service_name=\"pydantic-basic-mcp-factory\")\r\n+logfire.instrument_mcp()\r\n+logfire.instrument_pydantic_ai()\r\n+\r\n+# Preset model aliases demonstrating different provider formats\r\n+MODEL_ALIASES = {\r\n+ # Standard providers (auto-detected)\r\n+ \"gemini\": \"gemini-2.5-pro-preview-06-05\",\r\n+ \"gemini-flash\": \"gemini-2.5-flash\",\r\n+ \"gpt4\": \"gpt-4\",\r\n+ \"claude\": \"claude-3-opus-20240229\",\r\n+ \r\n+ # Explicit provider prefix\r\n+ \"openai-o4\": \"openai:o4-mini\",\r\n+ \"deepseek\": \"deepseek:deepseek-chat\",\r\n+ \"deepseek-reasoner\": \"deepseek:deepseek-reasoner\",\r\n+ \r\n+ # AWS Bedrock models\r\n+ \"bedrock-nova\": \"bedrock:us.amazon.nova-pro-v1:0\",\r\n+ \"bedrock-claude\": \"bedrock:us.anthropic.claude-3-5-sonnet-20240620-v1:0\",\r\n+ \r\n+ # GitHub Models (free tier)\r\n+ \"github-grok\": \"github:xai/grok-3-mini\",\r\n+ \r\n+ # Local models via Ollama\r\n+ \"ollama-llama\": \"ollama:llama3.2\",\r\n+ \"ollama-qwen\": \"ollama:qwen2.5-coder:7b\",\r\n+ \r\n+ # Other providers\r\n+ \"perplexity\": \"perplexity:sonar-pro\",\r\n+ \"grok\": \"grok:grok-2-1212\",\r\n+}\r\n+\r\n+\r\n+def get_mcp_server() -> MCPServerStdio:\r\n+ \"\"\"Get the MCP server configuration.\"\"\"\r\n+ return MCPServerStdio(\r\n+ command=\"uv\",\r\n+ args=[\r\n+ \"run\",\r\n+ str(get_mcp_server_path(\"example_server.py\")),\r\n+ \"stdio\",\r\n+ ],\r\n+ )\r\n+\r\n+\r\n+async def main(\r\n+ model: str = \"gemini-2.5-pro-preview-06-05\",\r\n+ query: str = \"Greet Andrew and give him the current time\",\r\n+ provider_kwargs: Optional[dict] = None\r\n+) -> None:\r\n+ \"\"\"Runs the Pydantic agent with a given query using the model factory.\r\n+\r\n+ This function demonstrates how to use the model factory to create agents\r\n+ with different providers while maintaining the same MCP functionality.\r\n+\r\n+ Args:\r\n+ model: The model identifier (e.g., \"gemini-2.5-pro\", \"deepseek:deepseek-chat\")\r\n+ query: The query to run the agent with\r\n+ provider_kwargs: Optional provider-specific configuration (e.g., for Azure)\r\n+ \"\"\"\r\n+ # Create MCP server\r\n+ server = get_mcp_server()\r\n+ \r\n+ # Create agent using the factory\r\n+ print(f\"Creating agent with model: {model}\")\r\n+ agent = create_agent(\r\n+ model=model,\r\n+ mcp_servers=[server],\r\n+ provider_kwargs=provider_kwargs\r\n+ )\r\n+ \r\n+ # Run the agent\r\n+ try:\r\n+ async with agent.run_mcp_servers():\r\n+ result = await agent.run(query)\r\n+ print(f\"\\nResult from {model}:\")\r\n+ print(result.output)\r\n+ \r\n+ # Print usage information if available\r\n+ usage = result.usage()\r\n+ if usage:\r\n+ print(f\"\\nToken usage: {usage.total_tokens} total \"\r\n+ f\"({usage.request_tokens} in, {usage.response_tokens} out)\")\r\n+ \r\n+ except Exception as e:\r\n+ print(f\"Error running agent: {e}\")\r\n+ print(\"\\nMake sure you have the required API keys set:\")\r\n+ print(\"- GEMINI_API_KEY for Gemini models\")\r\n+ print(\"- OPENAI_API_KEY for OpenAI models\")\r\n+ print(\"- DEEPSEEK_API_KEY for DeepSeek models\")\r\n+ print(\"- AWS credentials for Bedrock models\")\r\n+ print(\"- Other provider-specific keys as needed\")\r\n+\r\n+\r\n+def parse_arguments():\r\n+ \"\"\"Parse command-line arguments.\"\"\"\r\n+ parser = argparse.ArgumentParser(\r\n+ description=\"Run Pydantic MCP agent with different model providers\",\r\n+ formatter_class=argparse.RawDescriptionHelpFormatter,\r\n+ epilog=\"\"\"\r\n+Examples:\r\n+ # Use default Gemini model\r\n+ python pydantic_mcp_factory.py\r\n+ \r\n+ # Use a specific model by alias\r\n+ python pydantic_mcp_factory.py --model deepseek\r\n+ \r\n+ # Use a model by full name\r\n+ python pydantic_mcp_factory.py --model \"openai:gpt-4\"\r\n+ \r\n+ # Use a custom query\r\n+ python pydantic_mcp_factory.py --query \"What's the weather like?\"\r\n+ \r\n+ # List available model aliases\r\n+ python pydantic_mcp_factory.py --list-models\r\n+ \"\"\"\r\n+ )\r\n+ \r\n+ parser.add_argument(\r\n+ \"--model\", \"-m\",\r\n+ default=\"gemini\",\r\n+ help=\"Model to use (alias or full model string)\"\r\n+ )\r\n+ \r\n+ parser.add_argument(\r\n+ \"--query\", \"-q\",\r\n+ default=\"Greet Andrew and give him the current time\",\r\n+ help=\"Query to send to the agent\"\r\n+ )\r\n+ \r\n+ parser.add_argument(\r\n+ \"--list-models\", \"-l\",\r\n+ action=\"store_true\",\r\n+ help=\"List available model aliases and exit\"\r\n+ )\r\n+ \r\n+ # Provider-specific options\r\n+ parser.add_argument(\r\n+ \"--azure-endpoint\",\r\n+ help=\"Azure endpoint for Azure AI Foundry models\"\r\n+ )\r\n+ \r\n+ parser.add_argument(\r\n+ \"--azure-api-version\",\r\n+ help=\"Azure API version for Azure AI Foundry models\"\r\n+ )\r\n+ \r\n+ parser.add_argument(\r\n+ \"--ollama-base-url\",\r\n+ default=\"http://localhost:11434/v1\",\r\n+ help=\"Base URL for Ollama server\"\r\n+ )\r\n+ \r\n+ return parser.parse_args()\r\n+\r\n+\r\n+if __name__ == \"__main__\":\r\n+ args = parse_arguments()\r\n+ \r\n+ # List models and exit if requested\r\n+ if args.list_models:\r\n+ print(\"Available model aliases:\")\r\n+ print(\"-\" * 50)\r\n+ for alias, model_string in MODEL_ALIASES.items():\r\n+ print(f\"{alias:<20} -> {model_string}\")\r\n+ print(\"\\nYou can also use any model string directly, e.g.:\")\r\n+ print(\" openrouter:google/gemini-2.5-pro-preview\")\r\n+ print(\" fireworks:accounts/fireworks/models/qwq-32b\")\r\n+ print(\" together:meta-llama/Llama-3.3-70B-Instruct-Turbo-Free\")\r\n+ exit(0)\r\n+ \r\n+ # Resolve model alias if applicable\r\n+ model = MODEL_ALIASES.get(args.model, args.model)\r\n+ \r\n+ # Build provider kwargs if Azure options are provided\r\n+ provider_kwargs = {}\r\n+ if args.azure_endpoint:\r\n+ provider_kwargs[\"azure_endpoint\"] = args.azure_endpoint\r\n+ if args.azure_api_version:\r\n+ provider_kwargs[\"api_version\"] = args.azure_api_version\r\n+ if args.ollama_base_url and model.startswith(\"ollama:\"):\r\n+ provider_kwargs[\"base_url\"] = args.ollama_base_url\r\n+ \r\n+ # Run the main function\r\n+ asyncio.run(main(\r\n+ model=model,\r\n+ query=args.query,\r\n+ provider_kwargs=provider_kwargs if provider_kwargs else None\r\n+ ))\r\ndiff --git a/agents_mcp_usage/evaluations/mermaid_evals/README.md b/agents_mcp_usage/evaluations/mermaid_evals/README.md\r\nindex ee33e22..a5645af 100644\r\n--- a/agents_mcp_usage/evaluations/mermaid_evals/README.md\r\n+++ b/agents_mcp_usage/evaluations/mermaid_evals/README.md\r\n@@ -64,8 +64,9 @@ The evaluation system uses Pydantic-AI and requires environment variables for AP\r\n # Required for most models\r\n export GEMINI_API_KEY=\"your-key-here\"\r\n \r\n-# Optional for specific models\r\n+# Optional for specific providers/models\r\n export OPENAI_API_KEY=\"your-key-here\"\r\n+export OPENROUTER_API_KEY=\"your-key-here\" # required for `openrouter:*` models\r\n ```\r\n \r\n ## Quick Start Guide\r\n@@ -80,6 +81,12 @@ uv run agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py\r\n ```bash\r\n # Run evaluation across multiple models in parallel\r\n uv run agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py\r\n+\r\n+# Example: include an OpenRouter model (requires OPENROUTER_API_KEY)\r\n+uv run agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py \\\r\n+ --models \"openrouter:anthropic/claude-3.7-sonnet,gemini-3-flash-preview\" \\\r\n+ --runs 5 \\\r\n+ --sequential\r\n ```\r\n \r\n ### 3. View Results in Local Dashboard (Recommended)\r\n@@ -370,12 +377,71 @@ uv run agents_mcp_usage/evaluations/mermaid_evals/scripts/merge_benchmark_result\r\n \r\n ## Monitoring & Debugging\r\n \r\n+### Logfire (remote, if configured)\r\n+\r\n All evaluation runs are traced with **Logfire** for comprehensive monitoring:\r\n - Tool call traces\r\n - Retry attempts and reasons\r\n - Execution durations\r\n - Categorised failure analysis\r\n \r\n+### Local per-case debug traces (JSON)\r\n+\r\n+When diagnosing failure modes across models (e.g. tool-call loops, request/turn limits, redundant tool usage), you can enable a local debug mode that writes a detailed per-case trace JSON file for each model attempt.\r\n+\r\n+Enable traces:\r\n+\r\n+```bash\r\n+uv run agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py \\\r\n+ --runs 1 \\\r\n+ --sequential \\\r\n+ --debug-traces\r\n+```\r\n+\r\n+Optional: choose where traces are written:\r\n+\r\n+```bash\r\n+uv run agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py \\\r\n+ --runs 1 \\\r\n+ --sequential \\\r\n+ --debug-traces \\\r\n+ --trace-dir ./mermaid_eval_results/debug_traces\r\n+```\r\n+\r\n+By default, the trace directory is:\r\n+\r\n+- `/debug_traces/` (where `` defaults to `./mermaid_eval_results`)\r\n+\r\n+#### Output layout\r\n+\r\n+Traces are stored under a per-model subdirectory:\r\n+\r\n+```text\r\n+/\r\n+ /\r\n+ model=__run###__attempt###__case=.json\r\n+```\r\n+\r\n+> `run###` is the planned run number (1-based), `attempt###` is the runner-level retry attempt (1-based), and `case_slug` is derived from the case name (or a hash of the input if absent).\r\n+\r\n+#### What gets captured\r\n+\r\n+Each trace JSON file includes (at minimum):\r\n+\r\n+- input: invalid diagram + hash + case name\r\n+- prompt query sent to the agent\r\n+- full message history (including tool call args and tool return payloads)\r\n+- derived message summary:\r\n+ - request count, response count\r\n+ - tool call/return counts\r\n+ - tool name sequence (helps spot loops)\r\n+- usage limits used for the run\r\n+- usage/tokens (when available from the provider)\r\n+- output text and extracted mermaid diagram (on success)\r\n+- exception metadata and traceback (on failures)\r\n+\r\n+**Important:** traces contain full prompts and full tool return payloads. Treat them as sensitive and avoid sharing publicly.\r\n+\r\n ## Troubleshooting\r\n \r\n ### Common Issues\r\ndiff --git a/agents_mcp_usage/evaluations/mermaid_evals/costs.json b/agents_mcp_usage/evaluations/mermaid_evals/costs.json\r\nindex 98fd3b9..4dd6a6f 100644\r\n--- a/agents_mcp_usage/evaluations/mermaid_evals/costs.json\r\n+++ b/agents_mcp_usage/evaluations/mermaid_evals/costs.json\r\n@@ -1,10 +1,44 @@\r\n {\r\n- \"_comment\": \"Prices are per 1 million tokens\",\r\n+ \"_comment\": \"Prices are per 1 million tokens - Updated July 19, 2025\",\r\n \"_sources\": {\r\n \"gemini\": \"https://ai.google.dev/gemini-api/docs/pricing\",\r\n- \"openai\": \"https://openai.com/api/pricing/\"\r\n+ \"openai\": \"https://openai.com/api/pricing/\",\r\n+ \"bedrock\": \"https://aws.amazon.com/bedrock/pricing/\",\r\n+ \"deepseek\": \"https://platform.deepseek.com/api-docs/pricing\",\r\n+ \"openrouter\": \"https://openrouter.ai/models\",\r\n+ \"github\": \"https://github.com/marketplace/models\",\r\n+ \"grok\": \"https://console.x.ai/pricing\",\r\n+ \"perplexity\": \"https://www.perplexity.ai/api/pricing\",\r\n+ \"fireworks\": \"https://fireworks.ai/pricing\",\r\n+ \"together\": \"https://www.together.ai/pricing\",\r\n+ \"heroku\": \"https://www.heroku.com/ai\"\r\n },\r\n \"model_costs\": {\r\n+ \"gemini-3-pro-preview\": {\r\n+ \"friendly_name\": \"Gemini 3.0 Pro Preview (Nov)\",\r\n+ \"input\": [\r\n+ {\r\n+ \"up_to\": 200000,\r\n+ \"price\": 2.0\r\n+ },\r\n+ {\r\n+ \"up_to\": \"inf\",\r\n+ \"price\": 4.0\r\n+ }\r\n+ ],\r\n+ \"output\": {\r\n+ \"default\": [\r\n+ {\r\n+ \"up_to\": 200000,\r\n+ \"price\": 12.0\r\n+ },\r\n+ {\r\n+ \"up_to\": \"inf\",\r\n+ \"price\": 18.0\r\n+ }\r\n+ ]\r\n+ }\r\n+ },\r\n \"gemini-2.5-pro-preview-03-25\": {\r\n \"friendly_name\": \"Gemini 2.5 Pro Preview (Mar)\",\r\n \"input\": [\r\n@@ -266,12 +300,29 @@\r\n ]\r\n }\r\n },\r\n+ \"gemini-3-flash-preview\": {\r\n+ \"friendly_name\": \"Gemini 3 Flash Preview\",\r\n+ \"input\": [\r\n+ {\r\n+ \"up_to\": \"inf\",\r\n+ \"price\": 0.5\r\n+ }\r\n+ ],\r\n+ \"output\": {\r\n+ \"default\": [\r\n+ {\r\n+ \"up_to\": \"inf\",\r\n+ \"price\": 3.0\r\n+ }\r\n+ ]\r\n+ }\r\n+ },\r\n \"gemini-2.5-flash\": {\r\n \"friendly_name\": \"Gemini 2.5 Flash\",\r\n \"input\": [\r\n {\r\n \"up_to\": \"inf\",\r\n- \"price\": 0.3\r\n+ \"price\": 0.15\r\n }\r\n ],\r\n \"output\": {\r\n@@ -295,7 +346,13 @@\r\n \"default\": [\r\n {\r\n \"up_to\": \"inf\",\r\n- \"price\": 2.5\r\n+ \"price\": 0.6\r\n+ }\r\n+ ],\r\n+ \"thinking\": [\r\n+ {\r\n+ \"up_to\": \"inf\",\r\n+ \"price\": 3.5\r\n }\r\n ]\r\n }\r\n@@ -317,8 +374,8 @@\r\n ]\r\n }\r\n },\r\n- \"gemini-2.5-flash-lite\": {\r\n- \"friendly_name\": \"Gemini 2.5 Flash Lite\",\r\n+ \"gemini-2.5-flash-lite-preview-09-25\": {\r\n+ \"friendly_name\": \"Gemini 2.5 Flash Lite Preview (Sep)\",\r\n \"input\": [\r\n {\r\n \"up_to\": \"inf\",\r\n@@ -334,8 +391,8 @@\r\n ]\r\n }\r\n },\r\n- \"gemini-2.5-flash-lite-preview-09-2025\": {\r\n- \"friendly_name\": \"Gemini 2.5 Flash Lite Preview (Sept)\",\r\n+ \"gemini-2.5-flash-lite\": {\r\n+ \"friendly_name\": \"Gemini 2.5 Flash Lite\",\r\n \"input\": [\r\n {\r\n \"up_to\": \"inf\",\r\n@@ -373,14 +430,31 @@\r\n \"input\": [\r\n {\r\n \"up_to\": \"inf\",\r\n- \"price\": 10.0\r\n+ \"price\": 2.0\r\n+ }\r\n+ ],\r\n+ \"output\": {\r\n+ \"default\": [\r\n+ {\r\n+ \"up_to\": \"inf\",\r\n+ \"price\": 8.0\r\n+ }\r\n+ ]\r\n+ }\r\n+ },\r\n+ \"openai:o3-pro\": {\r\n+ \"friendly_name\": \"OpenAI o3-pro\",\r\n+ \"input\": [\r\n+ {\r\n+ \"up_to\": \"inf\",\r\n+ \"price\": 20.0\r\n }\r\n ],\r\n \"output\": {\r\n \"default\": [\r\n {\r\n \"up_to\": \"inf\",\r\n- \"price\": 40.0\r\n+ \"price\": 80.0\r\n }\r\n ]\r\n }\r\n@@ -436,6 +510,71 @@\r\n ]\r\n }\r\n },\r\n+ \"openai:gpt-5\": {\r\n+ \"friendly_name\": \"GPT-5\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 1.25}],\r\n+ \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 10.0}]}\r\n+ },\r\n+ \"openai:gpt-5-mini\": {\r\n+ \"friendly_name\": \"GPT-5 Mini\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 0.25}],\r\n+ \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 2.0}]}\r\n+ },\r\n+ \"openai:gpt-5-nano\": {\r\n+ \"friendly_name\": \"GPT-5 Nano\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 0.05}],\r\n+ \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 0.4}]}\r\n+ },\r\n+ \"openai:gpt-5-pro\": {\r\n+ \"friendly_name\": \"GPT-5 Pro\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 15.0}],\r\n+ \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 120.0}]}\r\n+ },\r\n+ \"openai:gpt-5-chat-latest\": {\r\n+ \"friendly_name\": \"GPT-5 Chat Latest\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 1.25}],\r\n+ \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 10.0}]}\r\n+ },\r\n+ \"openai:gpt-5-codex\": {\r\n+ \"friendly_name\": \"GPT-5 Codex\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 1.25}],\r\n+ \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 10.0}]}\r\n+ },\r\n+ \"openai:gpt-5.1\": {\r\n+ \"friendly_name\": \"GPT-5.1\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 1.25}],\r\n+ \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 10.0}]}\r\n+ },\r\n+ \"openai:gpt-5.1-codex\": {\r\n+ \"friendly_name\": \"GPT-5.1 Codex\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 1.25}],\r\n+ \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 10.0}]}\r\n+ },\r\n+ \"openai:gpt-5.1-codex-max\": {\r\n+ \"friendly_name\": \"GPT-5.1 Codex Max\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 1.25}],\r\n+ \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 10.0}]}\r\n+ },\r\n+ \"openai:gpt-5.1-codex-mini\": {\r\n+ \"friendly_name\": \"GPT-5.1 Codex Mini\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 0.25}],\r\n+ \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 2.0}]}\r\n+ },\r\n+ \"openai:gpt-5-search-api\": {\r\n+ \"friendly_name\": \"GPT-5 Search API\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 1.25}],\r\n+ \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 10.0}]}\r\n+ },\r\n+ \"openai:gpt-5.2\": {\r\n+ \"friendly_name\": \"GPT-5.2\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 1.75}],\r\n+ \"output\": {\"default\": [{\"up_to…2555 tokens truncated… }\r\n+}\r\n+```\r\n+\r\n+## Architecture\r\n+\r\n+### Components\r\n+\r\n+1. **parse_model_string()**: Extracts provider and model name from string\r\n+2. **create_model()**: Creates appropriate model instance or returns string for PydanticAI\r\n+3. **create_agent()**: Convenience function to create agents with proper model handling\r\n+4. **PROVIDER_CONFIGS**: Central configuration for all providers\r\n+\r\n+### Flow\r\n+\r\n+1. Model string is parsed to identify provider\r\n+2. If native provider (Google/OpenAI/Anthropic), return string for PydanticAI\r\n+3. If configured provider, create appropriate model instance\r\n+4. If unknown provider, return string and let PydanticAI handle it\r\n+5. Apply model profiles if specified in configuration\r\n+\r\n+## Benefits\r\n+\r\n+1. **Centralized Configuration**: All provider settings in one place\r\n+2. **Extensibility**: Easy to add new providers\r\n+3. **Consistency**: Same interface regardless of provider\r\n+4. **Flexibility**: Supports both native and custom providers\r\n+5. **Type Safety**: Leverages PydanticAI's type system\r\n+\r\n+## Example: Multi-Provider Comparison\r\n+\r\n+```python\r\n+from agents_mcp_usage.factory.model_factory import create_agent\r\n+\r\n+models = [\r\n+ \"gemini-2.5-pro\",\r\n+ \"openai:gpt-4\",\r\n+ \"deepseek:deepseek-chat\",\r\n+ \"ollama:llama3.2\"\r\n+]\r\n+\r\n+for model in models:\r\n+ agent = create_agent(\r\n+ model=model,\r\n+ mcp_servers=get_mcp_servers()\r\n+ )\r\n+ result = await agent.run(\"Solve this problem...\")\r\n+ print(f\"{model}: {result}\")\r\n+```\r\n+\r\n+## Environment Variables\r\n+\r\n+Most providers require API keys via environment variables:\r\n+\r\n+```bash\r\n+# Native providers\r\n+export GEMINI_API_KEY=\"...\"\r\n+export OPENAI_API_KEY=\"...\"\r\n+export ANTHROPIC_API_KEY=\"...\"\r\n+\r\n+ # Extended providers\r\n+ export DEEPSEEK_API_KEY=\"...\"\r\n+ export OPENROUTER_API_KEY=\"...\"\r\n+ export OPENROUTER_APP_URL=\"...\" # optional (app attribution)\r\n+ export OPENROUTER_APP_TITLE=\"...\" # optional (app attribution)\r\n+ export GITHUB_API_KEY=\"...\"\r\n+ export XAI_API_KEY=\"...\"\r\n+ export PERPLEXITY_API_KEY=\"...\"\r\n+\r\n+# AWS Bedrock\r\n+export AWS_REGION=\"us-east-1\"\r\n+export AWS_PROFILE=\"default\"\r\n+```\r\n+\r\n+## Migration Guide\r\n+\r\n+### Old Approach\r\n+```python\r\n+if model.startswith(\"bedrock:\"):\r\n+ from pydantic_ai.models.bedrock import BedrockConverseModel\r\n+ from pydantic_ai.providers.bedrock import BedrockProvider\r\n+ \r\n+ model_name = model.replace(\"bedrock:\", \"\")\r\n+ bedrock_model = BedrockConverseModel(\r\n+ model_name,\r\n+ provider=BedrockProvider(\r\n+ region_name=os.getenv(\"AWS_REGION\", \"us-east-1\"),\r\n+ profile_name=os.getenv(\"AWS_PROFILE\", \"default\")\r\n+ )\r\n+ )\r\n+ agent = Agent(bedrock_model, ...)\r\n+```\r\n+\r\n+### New Approach\r\n+```python\r\n+from agents_mcp_usage.factory.model_factory import create_agent\r\n+\r\n+agent = create_agent(\r\n+ model=\"bedrock:us.amazon.nova-pro-v1:0\",\r\n+ mcp_servers=get_mcp_servers()\r\n+)\r\n+```\r\n+\r\n+The model factory handles all the provider-specific logic internally, providing a cleaner and more maintainable approach.\r\ndiff --git a/agents_mcp_usage/factory/model_factory.py b/agents_mcp_usage/factory/model_factory.py\r\nnew file mode 100644\r\nindex 0000000..7b62fd2\r\n--- /dev/null\r\n+++ b/agents_mcp_usage/factory/model_factory.py\r\n@@ -0,0 +1,431 @@\r\n+\"\"\"\r\n+Model Factory for PydanticAI\r\n+\r\n+This module provides a centralized way to create models across different providers,\r\n+leveraging PydanticAI's built-in provider support and handling special cases.\r\n+\"\"\"\r\n+\r\n+import os\r\n+import re\r\n+from typing import Any, Dict, Optional, Union\r\n+from pydantic_ai import Agent, InlineDefsJsonSchemaTransformer\r\n+from pydantic_ai.models import Model\r\n+from pydantic_ai.models.openai import OpenAIResponsesModel\r\n+from pydantic_ai.profiles.openai import OpenAIModelProfile\r\n+from pydantic_ai.providers.openai import OpenAIProvider\r\n+\r\n+\r\n+# Providers that PydanticAI handles natively with provider:model syntax.\r\n+#\r\n+# NOTE:\r\n+# - `openrouter:*` is intentionally **not** treated as native in this repo.\r\n+# We construct an explicit OpenRouter provider + OpenAI-compatible model so we\r\n+# can control configuration and avoid relying on implicit provider detection.\r\n+NATIVE_PROVIDERS = {\r\n+ \"deepseek\", \"github\", \"grok\", \"azure\",\r\n+ \"fireworks\", \"together\", \"heroku\"\r\n+}\r\n+\r\n+# Special cases that need custom handling\r\n+PROVIDER_CONFIGS = {\r\n+ \"bedrock\": {\r\n+ # Bedrock uses a different model class\r\n+ \"handler\": \"bedrock_handler\",\r\n+ \"env_vars\": {\r\n+ \"region\": \"AWS_REGION\",\r\n+ \"profile\": \"AWS_PROFILE\"\r\n+ }\r\n+ },\r\n+ \"ollama\": {\r\n+ # Ollama is OpenAI-compatible but needs custom base URL\r\n+ \"handler\": \"openai_compatible_handler\",\r\n+ \"base_url\": \"http://localhost:11434/v1\",\r\n+ \"api_key\": \"not-needed\",\r\n+ \"profile\": {\r\n+ \"json_schema_transformer\": InlineDefsJsonSchemaTransformer,\r\n+ \"openai_supports_strict_tool_definition\": False\r\n+ }\r\n+ },\r\n+ \"perplexity\": {\r\n+ # Perplexity is OpenAI-compatible with custom base URL\r\n+ \"handler\": \"openai_compatible_handler\",\r\n+ \"base_url\": \"https://api.perplexity.ai\",\r\n+ \"env_var\": \"PERPLEXITY_API_KEY\"\r\n+ },\r\n+ \"openrouter\": {\r\n+ # OpenRouter has first-class support in PydanticAI, but we construct the\r\n+ # provider/model explicitly so configuration is controlled by this repo.\r\n+ \"handler\": \"openrouter_handler\",\r\n+ \"env_var\": \"OPENROUTER_API_KEY\",\r\n+ \"env_vars\": {\r\n+ \"app_url\": \"OPENROUTER_APP_URL\",\r\n+ \"app_title\": \"OPENROUTER_APP_TITLE\",\r\n+ },\r\n+ }\r\n+}\r\n+\r\n+\r\n+def ensure_openai_responses_model(model_string: str) -> str:\r\n+ \"\"\"Ensure OpenAI models use the Responses API.\r\n+\r\n+ This helper normalises any OpenAI model identifier to the\r\n+ `openai-responses:` provider namespace so that PydanticAI\r\n+ will use the OpenAI Responses API rather than Chat Completions.\r\n+\r\n+ Examples:\r\n+ \"gpt-5\" -> \"openai-responses:gpt-5\"\r\n+ \"openai:gpt-5-mini\" -> \"openai-responses:gpt-5-mini\"\r\n+ \"openai-responses:gpt-5\" -> \"openai-responses:gpt-5\" (unchanged)\r\n+ \"\"\"\r\n+ # Avoid double-prefixing if already using Responses API directly\r\n+ if model_string.startswith(\"openai-responses:\"):\r\n+ return model_string\r\n+\r\n+ provider, model_name = parse_model_string(model_string)\r\n+ if provider == \"openai\":\r\n+ return f\"openai-responses:{model_name}\"\r\n+\r\n+ return model_string\r\n+\r\n+\r\n+# Accept optional reasoning hints for models that support it\r\n+REASONING_SUFFIX_RE = re.compile(r\"\\s*\\((low|medium|high|none|minimal|xhigh)\\)\\s*$\", re.IGNORECASE)\r\n+\r\n+\r\n+def extract_reasoning_effort(model_string: str) -> tuple[str, Optional[str]]:\r\n+ \"\"\"Strip trailing reasoning hint like \"(medium)\" or \"(none)\" from model string.\r\n+\r\n+ Returns the base model string and the effort level (lowercased) if present.\r\n+ \"\"\"\r\n+\r\n+ match = REASONING_SUFFIX_RE.search(model_string)\r\n+ if not match:\r\n+ return model_string.strip(), None\r\n+\r\n+ effort = match.group(1).lower()\r\n+ base = REASONING_SUFFIX_RE.sub(\"\", model_string).strip()\r\n+ return base, effort\r\n+\r\n+\r\n+def parse_model_string(model_string: str) -> tuple[Optional[str], str]:\r\n+ \"\"\"\r\n+ Parse a model string to extract provider and model name.\r\n+ \r\n+ Args:\r\n+ model_string: Model identifier (e.g., \"openai:gpt-4\", \"deepseek:deepseek-chat\", \"gemini-2.5-pro\")\r\n+ \r\n+ Returns:\r\n+ Tuple of (provider, model_name)\r\n+ \"\"\"\r\n+ if \":\" in model_string:\r\n+ provider, model_name = model_string.split(\":\", 1)\r\n+ return provider, model_name\r\n+ \r\n+ # Check for known prefixes without colon\r\n+ if model_string.startswith(\"gemini-\"):\r\n+ return \"google\", model_string\r\n+ elif model_string.startswith(\"gpt-\") or model_string.startswith(\"o1-\") or model_string.startswith(\"o3-\") or model_string.startswith(\"o4-\"):\r\n+ return \"openai\", model_string\r\n+ elif model_string.startswith(\"claude-\"):\r\n+ return \"anthropic\", model_string\r\n+ \r\n+ # Default to no specific provider\r\n+ return None, model_string\r\n+\r\n+\r\n+def handle_bedrock_model(\r\n+ model_name: str,\r\n+ provider_kwargs: Optional[Dict[str, Any]] = None\r\n+) -> Model:\r\n+ \"\"\"Handle AWS Bedrock models which require special treatment.\"\"\"\r\n+ from pydantic_ai.models.bedrock import BedrockConverseModel\r\n+ from pydantic_ai.providers.bedrock import BedrockProvider\r\n+ \r\n+ config = PROVIDER_CONFIGS[\"bedrock\"]\r\n+ provider_kwargs = provider_kwargs or {}\r\n+ \r\n+ region = provider_kwargs.get(\"region_name\") or os.getenv(config[\"env_vars\"][\"region\"], \"us-east-1\")\r\n+ profile = provider_kwargs.get(\"profile_name\") or os.getenv(config[\"env_vars\"][\"profile\"], \"default\")\r\n+ \r\n+ provider_instance = BedrockProvider(\r\n+ region_name=region,\r\n+ profile_name=profile\r\n+ )\r\n+ \r\n+ return BedrockConverseModel(\r\n+ model_name,\r\n+ provider=provider_instance\r\n+ )\r\n+\r\n+\r\n+def handle_openai_compatible(\r\n+ provider: str,\r\n+ model_name: str,\r\n+ provider_kwargs: Optional[Dict[str, Any]] = None\r\n+) -> Model:\r\n+ \"\"\"Handle OpenAI-compatible providers that need custom configuration.\"\"\"\r\n+ config = PROVIDER_CONFIGS[provider]\r\n+ provider_kwargs = provider_kwargs or {}\r\n+ \r\n+ # Build OpenAI provider kwargs\r\n+ openai_kwargs = {}\r\n+ \r\n+ # Set base URL\r\n+ if \"base_url\" in config:\r\n+ openai_kwargs[\"base_url\"] = provider_kwargs.get(\"base_url\", config[\"base_url\"])\r\n+ \r\n+ # Handle API key\r\n+ if \"api_key\" in config:\r\n+ openai_kwargs[\"api_key\"] = config[\"api_key\"]\r\n+ elif \"env_var\" in config:\r\n+ api_key = provider_kwargs.get(\"api_key\") or os.getenv(config[\"env_var\"])\r\n+ if api_key:\r\n+ openai_kwargs[\"api_key\"] = api_key\r\n+ \r\n+ # Create OpenAI provider\r\n+ provider_instance = OpenAIProvider(**openai_kwargs)\r\n+ \r\n+ # Create model with profile if specified\r\n+ if config.get(\"profile\"):\r\n+ profile = OpenAIModelProfile(**config[\"profile\"])\r\n+ return OpenAIResponsesModel(\r\n+ model_name,\r\n+ provider=provider_instance,\r\n+ profile=profile,\r\n+ )\r\n+ else:\r\n+ return OpenAIResponsesModel(\r\n+ model_name,\r\n+ provider=provider_instance,\r\n+ )\r\n+\r\n+\r\n+def handle_openrouter_model(\r\n+ model_name: str,\r\n+ provider_kwargs: Optional[Dict[str, Any]] = None,\r\n+) -> Model:\r\n+ \"\"\"Handle OpenRouter models via an explicit provider/model.\r\n+\r\n+ PydanticAI can accept `openrouter:` strings directly, but we build\r\n+ the provider/model explicitly so this repository owns configuration and can\r\n+ support optional app attribution.\r\n+ \"\"\"\r\n+\r\n+ # Import lazily so users without the optional dependency can still import\r\n+ # the rest of the repo.\r\n+ #\r\n+ # NOTE: In pydantic-ai-slim==1.17.x, OpenRouter uses the OpenAI-compatible\r\n+ # models (Chat Completions) with an `OpenRouterProvider`, rather than a\r\n+ # dedicated `OpenRouterModel` class.\r\n+ import json\r\n+\r\n+ from openai import AsyncOpenAI\r\n+ from openai.types import chat\r\n+ from pydantic import ValidationError\r\n+ from pydantic_ai.models.openai import OpenAIChatModel\r\n+ from pydantic_ai.providers.openrouter import OpenRouterProvider\r\n+\r\n+ class OpenRouterPatchedChatModel(OpenAIChatModel):\r\n+ \"\"\"OpenAIChatModel with OpenRouter response sanitation.\r\n+\r\n+ Some OpenRouter-backed models (notably Anthropic) occasionally return\r\n+ invalid tool-call payloads such as `function.arguments = null`. PydanticAI\r\n+ validates the OpenAI response strictly, which raises\r\n+ `UnexpectedModelBehavior` before tool execution can happen.\r\n+\r\n+ We patch the raw ChatCompletion dict prior to Pydantic validation.\r\n+ \"\"\"\r\n+\r\n+ def _process_response(self, response: chat.ChatCompletion | str): # type: ignore[override]\r\n+ if isinstance(response, chat.ChatCompletion):\r\n+ data = response.model_dump()\r\n+\r\n+ # Fix invalid tool_calls payloads (e.g. `arguments: null`)\r\n+ try:\r\n+ choices = data.get(\"choices\")\r\n+ if isinstance(choices, list):\r\n+ for choice in choices:\r\n+ if not isinstance(choice, dict):\r\n+ continue\r\n+ message = choice.get(\"message\")\r\n+ if not isinstance(message, dict):\r\n+ continue\r\n+ tool_calls = message.get(\"tool_calls\")\r\n+ if not isinstance(tool_calls, list):\r\n+ continue\r\n+ for call in tool_calls:\r\n+ if not isinstance(call, dict):\r\n+ continue\r\n+ if call.get(\"type\") != \"function\":\r\n+ continue\r\n+ fn = call.get(\"function\")\r\n+ if not isinstance(fn, dict):\r\n+ fn = {}\r\n+ call[\"function\"] = fn\r\n+ args = fn.get(\"arguments\")\r\n+ if args is None:\r\n+ fn[\"arguments\"] = \"{}\"\r\n+ elif not isinstance(args, str):\r\n+ fn[\"arguments\"] = json.dumps(args)\r\n+ except Exception:\r\n+ # Best-effort only; fall through to default handling.\r\n+ pass\r\n+\r\n+ try:\r\n+ response = chat.ChatCompletion.model_validate(data)\r\n+ except ValidationError:\r\n+ # If sanitation didn't help, let the base model raise a\r\n+ # helpful UnexpectedModelBehavior.\r\n+ pass\r\n+\r\n+ return super()._process_response(response)\r\n+\r\n+ config = PROVIDER_CONFIGS[\"openrouter\"]\r\n+ provider_kwargs = provider_kwargs or {}\r\n+\r\n+ api_key = provider_kwargs.get(\"api_key\") or os.getenv(config[\"env_var\"])\r\n+ if not api_key:\r\n+ raise ValueError(\r\n+ \"OpenRouter API key not configured. \"\r\n+ \"Set OPENROUTER_API_KEY or pass provider_kwargs={'api_key': '...'}\"\r\n+ )\r\n+\r\n+ app_url = provider_kwargs.get(\"app_url\") or os.getenv(config[\"env_vars\"][\"app_url\"])\r\n+ app_title = provider_kwargs.get(\"app_title\") or os.getenv(config[\"env_vars\"][\"app_title\"])\r\n+\r\n+ # OpenRouter app attribution uses HTTP headers:\r\n+ # - HTTP-Referer: your app URL\r\n+ # - X-Title: your app name\r\n+ # See: https://openrouter.ai/docs/app-attribution\r\n+ default_headers: dict[str, str] = {}\r\n+ if app_url:\r\n+ default_headers[\"HTTP-Referer\"] = app_url\r\n+ if app_title:\r\n+ default_headers[\"X-Title\"] = app_title\r\n+\r\n+ base_url = provider_kwargs.get(\"base_url\") or \"https://openrouter.ai/api/v1\"\r\n+ openai_client = AsyncOpenAI(\r\n+ base_url=base_url,\r\n+ api_key=api_key,\r\n+ default_headers=default_headers or None,\r\n+ )\r\n+ provider_instance = OpenRouterProvider(openai_client=openai_client)\r\n+\r\n+ return OpenRouterPatchedChatModel(model_name, provider=provider_instance)\r\n+\r\n+\r\n+def create_model(\r\n+ model_string: str,\r\n+ model_settings: Optional[Dict[str, Any]] = None,\r\n+ provider_kwargs: Optional[Dict[str, Any]] = None\r\n+) -> Union[Model, str]:\r\n+ \"\"\"\r\n+ Create a model instance from a model string.\r\n+ \r\n+ This function:\r\n+ 1. Returns the string directly for providers PydanticAI handles natively\r\n+ 2. Handles special cases like Bedrock and OpenAI-compatible providers\r\n+ 3. Falls back to returning the string for unknown providers\r\n+ \r\n+ Args:\r\n+ model_string: Model identifier\r\n+ model_settings: Optional model-specific settings\r\n+ provider_kwargs: Optional provider-specific kwargs (e.g., azure_endpoint)\r\n+ \r\n+ Returns:\r\n+ Either a Model instance or the original string for PydanticAI to handle\r\n+ \"\"\"\r\n+ # First, normalise any OpenAI models to use the Responses API\r\n+ model_string = ensure_openai_responses_model(model_string)\r\n+\r\n+ provider, model_name = parse_model_string(model_string)\r\n+ \r\n+ # No provider prefix - let PydanticAI handle it\r\n+ if provider is None:\r\n+ return model_string\r\n+ \r\n+ # Standard providers that PydanticAI auto-detects\r\n+ # Note: OpenAI models have been converted to \"openai-responses:*\" above\r\n+ if provider in [\"google\", \"anthropic\"]:\r\n+ return model_string\r\n+ \r\n+ # Providers with native PydanticAI support\r\n+ if provider in NATIVE_PROVIDERS:\r\n+ # Special validation for Azure\r\n+ if provider == \"azure\" and provider_kwargs:\r\n+ required = [\"azure_endpoint\", \"api_version\"]\r\n+ missing = [k for k in required if k not in provider_kwargs]\r\n+ if missing:\r\n+ raise ValueError(\r\n+ f\"Azure provider requires {missing} in provider_kwargs. \"\r\n+ f\"Alternatively, let PydanticAI handle it by using the string directly.\"\r\n+ )\r\n+ # Return the string - PydanticAI will handle it\r\n+ return model_string\r\n+ \r\n+ # Handle special cases\r\n+ if provider in PROVIDER_CONFIGS:\r\n+ config = PROVIDER_CONFIGS[provider]\r\n+ handler = config.get(\"handler\")\r\n+ \r\n+ if handler == \"bedrock_handler\":\r\n+ return handle_bedrock_model(model_name, provider_kwargs)\r\n+ elif handler == \"openai_compatible_handler\":\r\n+ return handle_openai_compatible(provider, model_name, provider_kwargs)\r\n+ elif handler == \"openrouter_handler\":\r\n+ return handle_openrouter_model(model_name, provider_kwargs)\r\n+ \r\n+ # Unknown provider - return string and let PydanticAI try\r\n+ return model_string\r\n+\r\n+\r\n+def create_agent(\r\n+ model: Union[str, Model],\r\n+ mcp_servers=None,\r\n+ model_settings: Optional[Dict[str, Any]] = None,\r\n+ provider_kwargs: Optional[Dict[str, Any]] = None,\r\n+ **agent_kwargs\r\n+) -> Agent:\r\n+ \"\"\"\r\n+ Create an Agent with proper model handling.\r\n+ \r\n+ This is a convenience function that:\r\n+ 1. Converts model strings to appropriate Model instances when needed\r\n+ 2. Passes through Model instances unchanged\r\n+ 3. Leverages PydanticAI's native provider support\r\n+ \r\n+ Args:\r\n+ model: Model string or Model instance\r\n+ mcp_servers: Optional MCP servers\r\n+ model_settings: Optional model settings\r\n+ provider_kwargs: Optional provider-specific kwargs\r\n+ **agent_kwargs: Additional Agent kwargs\r\n+ \r\n+ Returns:\r\n+ Configured Agent instance\r\n+ \"\"\"\r\n+ if isinstance(model, str):\r\n+ base_model, reasoning_effort = extract_reasoning_effort(model)\r\n+\r\n+ # Ensure we have a dict to attach model-level settings\r\n+ if model_settings is None:\r\n+ model_settings = {}\r\n+\r\n+ # Only apply reasoning hint to OpenAI Responses-capable models\r\n+ provider, _ = parse_model_string(base_model)\r\n+ if (\r\n+ reasoning_effort\r\n+ and isinstance(model_settings, dict)\r\n+ and provider in {\"openai\", \"openai-responses\"}\r\n+ and \"openai_reasoning_effort\" not in model_settings\r\n+ ):\r\n+ model_settings[\"openai_reasoning_effort\"] = reasoning_effort\r\n+\r\n+ model = create_model(base_model, model_settings, provider_kwargs)\r\n+\r\n+ return Agent(\r\n+ model,\r\n+ mcp_servers=mcp_servers,\r\n+ model_settings=model_settings if isinstance(model, str) else None,\r\n+ **agent_kwargs\r\n+ )\r\n","/bin/bash -lc 'git diff 697c3fc..HEAD -- README.md agents_mcp_usage/basic_mcp/README.md agents_mcp_usage/evaluations/mermaid_evals/README.md agents_mcp_usage/evaluations/mermaid_evals/costs.json agents_mcp_usage/factory/MODEL_FACTORY.md agents_mcp_usage/factory/model_factory.py agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py'",[1661],"34338",{"_733":1662,"_90":1590},"git diff 697c3fc..HEAD -- README.md agents_mcp_usage/basic_mcp/README.md agents_mcp_usage/evaluations/mermaid_evals/README.md agents_mcp_usage/evaluations/mermaid_evals/costs.json agents_mcp_usage/factory/MODEL_FACTORY.md agents_mcp_usage/factory/model_factory.py agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py",{"_670":1664,"_662":663,"_672":81},{"_731":-5,"_733":1658,"_735":1665,"_737":738,"_739":-5,"_741":-5,"_41":1652,"_742":1660,"_744":745,"_667":755,"_90":746},[1666],{"_733":1662,"_90":1590},{"_670":1668,"_662":663,"_672":81},{"_759":1669,"_722":1652,"_41":-5,"_297":761,"_762":-5,"_90":763},"{\"cmd\":\"git diff 697c3fc..HEAD -- README.md agents_mcp_usage/basic_mcp/README.md agents_mcp_usage/evaluations/mermaid_evals/README.md agents_mcp_usage/evaluations/mermaid_evals/costs.json agents_mcp_usage/factory/MODEL_FACTORY.md agents_mcp_usage/factory/model_factory.py agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py\"}",{"_670":1671,"_662":663,"_672":81},{"_673":-5,"_691":1672,"_41":1673,"_694":1674,"_90":696},"gAAAAABpaAiamJ6HLjjNciskyhqLqhCfjLiPUM-lX6MIutdhkUCMuYLXPlNQQwBRV07vW6LahjE7XZXcw2td0ohWj_1d2wa29ARAi6YJMN1w1uLhmDBXe0fzKI1qelm_C--SPVgofvymhOae63uj6w4gkBrrn_JH2h-CBkXPPoqGlIZ7GijqlERdTdxRIb26hJ9abL9DrhBE3O1a8eJP5-MqYk0QShocoe00tgN9l-5yWMN57ar9lx8VsB6GQdko4YTiFvitUCMzPkdxKWi8mOPA16R6POU_QW5prezb_lvHvkQViNeeiAkAC4Ss4sCf5HeVAYO6nasQMsEOVBz2Dupjpjbnw5BWuLz97L3Fdswx8k6Om96iK0sDEQklc7fA3WUFH_KM16pIUPCBmCImN2Qp4tVwHYDCpx4Wu3-VBqxuzcTD3KqyXWrI-ICR0Ko7VRSqlYgagn-EUcjch1cAuWK7EwTal9tpjBmQ6gLCecEF41o5WfXeXCr7aOerSYbPOUnL1deMOmG-dFAmdx6eXsN7WMfRkKH5TCJaNqOs6eJVN3oxk-fw-hdGsNpny03O4ALL3JUSaRBEhkBuUxfeCF6VL_6H2QPyV_BeNmaGDZF0Kuh6TFlCT60pdKGk9vjG-S6J6fy5lXrNjayPIWBkc1rolCuiKITZI-fMZNYDDSOCQAO6icdnvb18xJvZH3vHMiuov5__M92Z2DoGKZuxZPdH4POdmwuc0SNoj1rh5PCory2oPZFBCH_B-ZZoYvdx4DktvdUEl_towqGEcKmVRK7dHvCG-0hwfAxXmjKELWgytNqVNHS5_rf5egVyCxAWv9w9hI_OvkRdNOhk2Gt77EEFX9Ytyh1k6yw_DJldhWbUhu7oIXySCj6lfx5Ia4wf55DZSTYlSuxV7X3KW7Tr8QY669HuIWj8dbNdkhYXFXF70vhuwu-_YJLDGjbVuXEk06Sv-LSFnX2nDz2g9NjTwzqsaz4R6P0X1qM2ClZZLlTyWaPPAdQGt9iBFiEFI7ZF17WspNaPCgPgXPsSZUIey4hhiQuaugmH03LTwQYKLYwvp-ZVdrwinu7_schUy3FQlFckuwo-4waiMOJIhnAhdPVWZz8uRvGfXaP3KCxt16sNpMbof5zH6Is6CCBKEohb7kqf3wyWxo-QQ11CfXeDwr5I7Y8mR1GANNEV56_jHDr68HxAVT3RccL4EfmY3UhJPLsxS9gVQvMXGW-LtV23BTsoC4hV-42ppHnF430BErr9JI-WWBoi0jyf2zLjwM07ArKM3T_EPtC_","legacy_responseItem_reasoning_12",[1675],{"_45":1676,"_90":700},"**Preparing detailed PR description**\n\nI'm gathering information from changed files, diffs, and relevant docs to clearly explain what was changed, why, how to verify it, and any impacts on schema or outputs.",{"_670":1678,"_662":663,"_672":81},{"_673":1679,"_41":1680,"_694":1681,"_90":696},[1676],"legacy_reasoning_11",[],{"_670":1683,"_662":663,"_672":81},{"_673":1684,"_41":1680,"_694":1685,"_90":696},[],[],{"_670":1687,"_662":663,"_672":81},{"_722":1688,"_724":1689,"_90":726},"call_n4HZW03Smq9UKF89FL7VzH6J",{"_408":1690,"_728":-5},"Chunk ID: e2944a\nWall time: 0.0514 seconds\nProcess exited with code 0\nOriginal token count: 341\nOutput:\n .env.example | 1 +\r\n AGENTS.md | 41 +\r\n README.md | 1 +\r\n agents_mcp_usage/basic_mcp/README.md | 29 +-\r\n .../basic_mcp_use/pydantic_mcp_factory.py | 216 +++\r\n .../evaluations/mermaid_evals/README.md | 68 +-\r\n .../evaluations/mermaid_evals/costs.json | 358 ++++-\r\n .../evaluations/mermaid_evals/dashboard_config.py | 17 +-\r\n .../mermaid_evals/evals_pydantic_mcp.py | 675 +++++++--\r\n .../evaluations/mermaid_evals/merbench_ui.py | 291 +++-\r\n .../evaluations/mermaid_evals/run_multi_evals.py | 622 +++++++--\r\n .../scripts/merge_benchmark_results.py | 553 ++++++++\r\n .../scripts/preprocess_merbench_data.py | 116 +-\r\n .../scripts/verify_retry_orchestration.py | 126 ++\r\n agents_mcp_usage/factory/MODEL_FACTORY.md | 255 ++++\r\n agents_mcp_usage/factory/__init__.py | 1 +\r\n agents_mcp_usage/factory/model_factory.py | 431 ++++++\r\n mcp_servers/mermaid_validator.py | 2 +-\r\n pyproject.toml | 2 +-\r\n uv.lock | 1434 +++++++++++---------\r\n 20 files changed, 4290 insertions(+), 949 deletions(-)\r\n",{"_670":1692,"_662":663,"_672":81},{"_731":1693,"_733":1694,"_735":1695,"_737":738,"_739":740,"_741":31,"_41":1688,"_742":1696,"_744":745,"_667":111,"_90":746}," .env.example | 1 +\r\n AGENTS.md | 41 +\r\n README.md | 1 +\r\n agents_mcp_usage/basic_mcp/README.md | 29 +-\r\n .../basic_mcp_use/pydantic_mcp_factory.py | 216 +++\r\n .../evaluations/mermaid_evals/README.md | 68 +-\r\n .../evaluations/mermaid_evals/costs.json | 358 ++++-\r\n .../evaluations/mermaid_evals/dashboard_config.py | 17 +-\r\n .../mermaid_evals/evals_pydantic_mcp.py | 675 +++++++--\r\n .../evaluations/mermaid_evals/merbench_ui.py | 291 +++-\r\n .../evaluations/mermaid_evals/run_multi_evals.py | 622 +++++++--\r\n .../scripts/merge_benchmark_results.py | 553 ++++++++\r\n .../scripts/preprocess_merbench_data.py | 116 +-\r\n .../scripts/verify_retry_orchestration.py | 126 ++\r\n agents_mcp_usage/factory/MODEL_FACTORY.md | 255 ++++\r\n agents_mcp_usage/factory/__init__.py | 1 +\r\n agents_mcp_usage/factory/model_factory.py | 431 ++++++\r\n mcp_servers/mermaid_validator.py | 2 +-\r\n pyproject.toml | 2 +-\r\n uv.lock | 1434 +++++++++++---------\r\n 20 files changed, 4290 insertions(+), 949 deletions(-)\r\n","/bin/bash -lc 'git diff --stat 697c3fc..HEAD'",[1697],"63876",{"_733":1698,"_90":1590},"git diff --stat 697c3fc..HEAD",{"_670":1700,"_662":663,"_672":81},{"_731":-5,"_733":1694,"_735":1701,"_737":738,"_739":-5,"_741":-5,"_41":1688,"_742":1696,"_744":745,"_667":755,"_90":746},[1702],{"_733":1698,"_90":1590},{"_670":1704,"_662":663,"_672":81},{"_759":1705,"_722":1688,"_41":-5,"_297":761,"_762":-5,"_90":763},"{\"cmd\":\"git diff --stat 697c3fc..HEAD\"}",{"_670":1707,"_662":663,"_672":81},{"_673":-5,"_691":1708,"_41":1709,"_694":1710,"_90":696},"gAAAAABpaAiWVVV-lArSA9wIjLf1Hoei50BKgpVtTcLVR-s0ITyeZ5GP3nO7Eu4AwTPXInrXllScRuUxasMpCI0p7W2f-iz15CwWAZ4bf6tqpVLGUwvsUJbXU8JBzXRIFIcFXztQpPzxWpFTlhkAkexaWnN9-T5O54OxV319KP_YM-v9_amz4R9VC0_zQ-zSYioYzcP4uR68Be5swjwd8HOUTuLHmnY6fdMEvFEM9FluI_lnkilkdzzPXOh45qrishu7ZYNydS5aOngjeEpl0mnxqkKWK1BwvC_1U5YDFWU3c6Z4tH7wcEI7Vn6M6VIUuXlZmlIYomkCQKia4rFSG2-T9aR-y2gdzUuhZB_4A0KQeScWVyJ1mqUrjblEQ62ttblJAVmpT1DAmEMGtUYYaLAylJV01rt0nnfoGk5xAsB803KsnLIaTyVRB4hDb8a8f_9fez9RdizIKKyM2Wyy9Wqn8TbZLclRtHKVHjo-_3SydwgNM2djWSJT2nvD5VA6IDN5Jfh40EzgAFdjaJDWI_HMjkqX3VJasWIXlPw2bXtgHmfqAbAyQnKmaPv_pg_TmohGxyHHQe4Gc8YF7RxTjB5UhmTQu1q4_MqxUeyoG21LRMgCq4JL6z3IYUWIxuJrexLMOs2e1sX3oW6B0uNRoi9Nkikf6ejQ6oKCa9jf3d0hIy9GF6HHPxw8x5_AlVm7TMsON2DQgdSi797wgxJCM_FMGVa_vszR4zkli0AxW5lm2uABfFTB4z09fOucGl6lDo_o0D24KXDKJPcjx9mu5vTQs4Pu6NcnFnU8ZDdKYw45mhNDDGV78uIVh6dql4Hp_FiJnPG-VqBZGjBGsMmTnvG4HWyoAG4FBhd9wIQkyBLreDlF-YqbnikxjXQp9hUQJAVdIgDsWF_30TL4JmytncdaQaXqvihetOYtuWjCmYWQZK8AtKbT-Hq08FPT7GlQ7Wr20RbRoft28mEfIw-jNCnf9vy2ycy3XSKBXN4s_EL-AmbTwPOWByV4eFEdT0IXVLvCGBUEPCoa8vW5jG0l_3VeIuDl4ELF0PE0ecoYVHkeZARWNTbXpw8=","legacy_responseItem_reasoning_10",[1711],{"_45":1712,"_90":700},"**Preparing PR description**\n\nI'm collecting commits and a diff summary between base commit 697c3fc and HEAD to generate a concise PR description.",{"_670":1714,"_662":663,"_672":81},{"_673":1715,"_41":1716,"_694":1717,"_90":696},[1712],"legacy_reasoning_9",[],{"_670":1719,"_662":663,"_672":81},{"_673":1720,"_41":1716,"_694":1721,"_90":696},[],[],{"_670":1723,"_662":663,"_672":81},{"_722":1724,"_724":1725,"_90":726},"call_uxDXlcNVds9JwMFuHr5c3ReW",{"_408":1726,"_728":-5},"Chunk ID: 74c956\nWall time: 0.0503 seconds\nProcess exited with code 0\nOriginal token count: 76\nOutput:\n6ab40f9 (HEAD -> work) feat: add OpenRouter\r\n9bf1c66 fix: clarify token labels and normalize token usage\r\n2890bef feat: add Gemini 3 & GPT-5 pricing, bump deps\r\n448b29c Merge branch 'main' of github.com:andrewginns/agents-mcp-usage\r\n697c3fc Merge pull request #16 from andrewginns/october-2025-updates\r\n",{"_670":1728,"_662":663,"_672":81},{"_731":1729,"_733":1730,"_735":1731,"_737":738,"_739":801,"_741":31,"_41":1724,"_742":1732,"_744":745,"_667":111,"_90":746},"6ab40f9 (HEAD -> work) feat: add OpenRouter\r\n9bf1c66 fix: clarify token labels and normalize token usage\r\n2890bef feat: add Gemini 3 & GPT-5 pricing, bump deps\r\n448b29c Merge branch 'main' of github.com:andrewginns/agents-mcp-usage\r\n697c3fc Merge pull request #16 from andrewginns/october-2025-updates\r\n","/bin/bash -lc 'git log --oneline -n 5'",[1733],"93565",{"_733":1734,"_90":1590},"git log --oneline -n 5",{"_670":1736,"_662":663,"_672":81},{"_731":-5,"_733":1730,"_735":1737,"_737":738,"_739":-5,"_741":-5,"_41":1724,"_742":1732,"_744":745,"_667":755,"_90":746},[1738],{"_733":1734,"_90":1590},{"_670":1740,"_662":663,"_672":81},{"_759":1741,"_722":1724,"_41":-5,"_297":761,"_762":-5,"_90":763},"{\"cmd\":\"git log --oneline -n 5\"}",{"_670":1743,"_662":663,"_672":81},{"_673":-5,"_691":1744,"_41":1745,"_694":1746,"_90":696},"gAAAAABpaAiTRXPIk6_Az88xLwiQu5VdNqJQOvg_GFs2B1ePGhQ33UUP2KLVKdz-6RIc22YlTHJZQvzXUAQ64OCxAvKX1g5E1zIlpcAyID63RxZrYGffAXkpxGZubtvx3JC0DuvW2X8evkySIcP9NGU-yQQceXE5Ju3dunIJBCLwDALDSsr0yzoATW5vKHgRDonp1KSoVtj_xmfX1ZX_DIqfmrIp03ySJLqIJ3zmp3G3nkWjwmKA7po0ARnQvzQKx6z-wXs0XTXdusmOCYoSWSorFa74k4F-9cpL6TKJrEWy1j34yKKdIhXdx9sQE2SkboGhr4MAzdwcbH2Mir2CBZou-wJWaftx9HOzXPlCfbVf6zA6Wzb194FM02makdQJK4OwY_-JHyg138Mbv2Sro8k1iU9VsZ9OQAqU7WLPONZLSyCkBaqJo67gVtlZIoQP_NTRnKBCzsrl6Zu9O0i5pLrS932tgRyYjt7HzmroFphxi17WXIU1vxVBp81nwRdnChYLuxzFOh-JWJKR-WE5cJY4u80AQ9-vg1Vm15vT-2zCgpipWzbs3G-YT5_qNyAI5BS8r9eLPmJs-brr6E0vihPY5fiw_pXYVOnYZzndZJjcgAjs0HHiHqfrzdHE1iz7ot4-je26-HiJJ48ZxWnfX8yWKtuchdbOk99nfyEramd524zWnBjIeTPryF46htC9x6XZL51k02qfaiDu9tmi3D-_6-dIqrPHsSzOlUbzyxtgY5KvIeNeh5X3UXrnEhTE03txJxaVq9WSllQ3OnpZYH7ZtNv_q3_Z9lQJB721PB-ks-UYidPBncuoUDm8ZhKlXmJpbY-YC8G1vL2lDlsLpRXmr_Iasg-p13busvrRAPnvcB6V-TCZ_uZe3CUAhPtoyqVJK5fwy0XOlbRrW8SSbMxGecW_S8zNjz8qdoOQHE-EnyIEfkbPQg1CPRlsTengp5G6BM5mxFfFrsZLTYCbJueZcFedHenlKcY_hJIlMdP4bl0Ck2EFMUluNqeNayLz3ZEK6chHnY3INok8z3Ud3qrO6d1K1YIoLA==","legacy_responseItem_reasoning_8",[1747],{"_45":1748,"_90":700},"**Checking git status and diff approach**\n\nI'm verifying if the branch has changes by checking if the git status is clean, then planning to use git diff against a base reference—likely derived from PR info or a commit—to get the necessary diff.",{"_670":1750,"_662":663,"_672":81},{"_673":1751,"_41":1752,"_694":1753,"_90":696},[1748],"legacy_reasoning_7",[],{"_670":1755,"_662":663,"_672":81},{"_673":1756,"_41":1752,"_694":1757,"_90":696},[],[],{"_670":1759,"_662":663,"_672":81},{"_722":1760,"_724":1761,"_90":726},"call_PgzSpzdgdiiEFIwbqi9S6gsh",{"_408":1762,"_728":-5},"Chunk ID: 3dede5\nWall time: 0.0514 seconds\nProcess exited with code 0\nOriginal token count: 3\nOutput:\n## work\r\n",{"_670":1764,"_662":663,"_672":81},{"_731":1765,"_733":1766,"_735":1767,"_737":738,"_739":740,"_741":31,"_41":1760,"_742":1768,"_744":745,"_667":111,"_90":746},"## work\r\n","/bin/bash -lc 'git status -sb'",[1769],"1686",{"_733":1770,"_90":1590},"git status 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-sb\"}",{"_670":1779,"_662":663,"_672":81},{"_673":-5,"_691":1780,"_41":1781,"_694":1782,"_90":696},"gAAAAABpaAiQbSlsmjA1_nmJXV9pqX0Jhag9xu-OZldcJ481QqS6IcedXX7wFqASBf44KCQEKVg2dNWoFVp1bLHlElira_MUt3-vdl_n1WRZ1EF_qSya5N6xfU470C_T1CCdNC3h3l2o4LxeWq2IfEL455q7q2HKs7BPxUljUH9ZrjicgTnfC6h1845dRMNUXmIQceir74bufkswYo8LvkaxW3kcREBBGcjQK6OLlEy5upJooCZZDNsUGq6cqeWOqygcQ1vS5YfX7OeVycOSvaBCJyY48u9Q23q8ckcHNVDA5EPo8ZcA7t45v-BOzMkdx-dZlSDtPWMt6m02yqmgfeAkTX_jrgTsBuGL3U817bL3PW44i7Z1omxDiGqVIxVZiw7zVT3HIr5idSULHwIta1E3pCiS8CsHNijUfMJRD_h2G4pbpudHj7Hu6ILWmKDFJsnNYlClbAPHmCvsxz2p1ln9T_x-q7Pb3KBVaihhLSkPTRxh9VKBiAwQUzFe17nb4ZHuI5GJsp-wz1Eay0Vdu2h4RwN939rqifNEE_7EfF9i24i7P5acAMJGKH_gudF60uNpqJY_opL-OOwNYcX3T97cbgji2QTTLSbI7kL6cj-wXanEo4IKF5XODKuj59KHbURlS3sgenj72goor1uBQ8qkdNseTljkboGCOGDxiOSt3c2ykmerAcrbpkmHdVrRss0CNVRayQj7d9fQ1ZrmYHZjQ_LffrpNDDnPR-DeU88NnP4zSzdS8B406C2PEbJHy0F5UOSXp1-WG4I6FKIfpZnfMCyVTE3YVfIOaamioDbdi6QVULHRHj7bAhrPxDKVVNoKjIdeSTrXilb-mOY7jNvos6kiltNt2kKmo45GDEMsob89eCHNyCeUyX46MaowE9aHRKNbEA-GBtUrjGsWulM6_Jbc5IloOYg4qf0PLdrbrOZIYWRrwkD09a8xMcgGueKTZPqtGF11ezeDCumQKvTaYrLfYceCjg==","legacy_responseItem_reasoning_6",[1783],{"_45":1784,"_90":700},"**Preparing 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The PR title and message should summarize the changes **on top** of the original pull request.\n@codex create a PR description for me based on the changes in this branch.{\"title\":\"Jan/December 2025 updates\",\"body\":\"\",\"base_ref\":\"697c3fc37bc56ad09d4763dec630075e6cbc2eeb\",\"head_ref\":\"6ab40f95f9b3d7ac2330ad821d6ef79dd101844b\",\"merge_commit_sha\":null,\"type\":\"pull_request_info\"}","text_elements",[],{"_670":1878,"_662":663,"_672":81},{"_673":1879,"_41":1871,"_90":1872},[1880],{"_45":1874,"_1875":1881,"_90":45},[],{"_670":1883,"_662":663,"_672":81},{"_673":1884,"_675":-5,"_41":-5,"_676":-5,"_94":420,"_90":677},[1885],{"_45":1874,"_90":1886},"input_text",{"_670":1888,"_662":663,"_672":81},{"_673":1889,"_675":-5,"_41":-5,"_676":-5,"_94":420,"_90":677},[1890],{"_45":1891,"_90":1886},"\n /workspace/agents-mcp-usage\n never\n danger-full-access\n enabled\n bash\n",{"_670":1893,"_662":663,"_672":81},{"_673":1894,"_675":-5,"_41":-5,"_676":-5,"_94":420,"_90":677},[1895],{"_45":1896,"_90":1886},"# AGENTS.md instructions for /workspace/agents-mcp-usage\n\n\n# Repository Guidelines\n\n## Project Structure & Module Organization\n- `agents_mcp_usage/` is the main Python package.\n - `basic_mcp/` contains **single-server** MCP demos across frameworks (`basic_mcp_use/`).\n - `multi_mcp/` contains **multi-server** MCP demos across frameworks (`multi_mcp_use/`).\n - `factory/` contains model/provider factory helpers (see `MODEL_FACTORY.md` and `model_factory.py`).\n - `evaluations/mermaid_evals/` holds the Merbench benchmark: runners (`evals_pydantic_mcp.py`, `run_multi_evals.py`), Streamlit UI (`merbench_ui.py`), scripts (`scripts/`), and config (`costs.json`, schemas).\n- `mcp_servers/` contains local MCP servers used by the agents (e.g., `example_server.py`, `mermaid_validator.py`).\n- `docs/` holds images and documentation assets.\n- Evaluation CSV/log outputs are written under `mermaid_eval_results//`.\n- Processed/merged dashboard datasets live under `agents_mcp_usage/evaluations/mermaid_evals/results/` (JSON).\n\n## Build, Test, and Development Commands\n- `make install` — install Python deps via `uv sync` and globally install Mermaid CLI via `npm` (needed for validation).\n- `make upgrade` — refresh deps (`uv sync -U`).\n- `make lint` — run `ruff` over the repo.\n- `make leaderboard` — launch Merbench Streamlit dashboard.\n- `make adk_basic_ui` / `make adk_multi_ui` — start Google ADK web UI for demos.\n- Example runs:\n - `uv run agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp.py`\n - `uv run agents_mcp_usage/basic_mcp/basic_mcp_use/oai-agent_mcp.py`\n - `uv run agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py --list-models`\n - `uv run agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py --runs 5 --parallel`\n\n## Coding Style & Naming Conventions\n- Python 3.13+; 4‑space indentation; prefer type hints throughout.\n- `snake_case` for functions/variables, `UpperCamelCase` for classes, constants in `UPPER_SNAKE_CASE`.\n- `ruff` is the linter of record; keep imports clean, avoid unused vars, and fix lint before PRs.\n\n## Testing Guidelines\n- No dedicated unit test suite yet. Validate changes by:\n - running the affected demo/eval script with `uv run …`, and/or\n - opening the dashboard on sample CSVs.\n- If you add reusable logic, consider introducing lightweight tests in a new `tests/` folder and wire them into the Makefile.\n\n## Commit & Pull Request Guidelines\n- Use Conventional Commit prefixes seen in history: `feat:`, `fix:`, `docs:`, `chore:`, `refactor:` (or `refact:`). 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output lines: 1076\n\nTotal output lines: 3101\n\ndiff --git a/agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py b/agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py\r\nindex 52f6baf..9b2e2d1 100644\r\n--- a/agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py\r\n+++ b/agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py\r\n@@ -15,31 +15,39 @@ This module is designed to be imported by multi-model evaluation scripts.\r\n \r\n import asyncio\r\n import csv\r\n+import hashlib\r\n+import importlib.util\r\n+import json\r\n import os\r\n import random\r\n+import re\r\n+import sys\r\n+import traceback\r\n+from dataclasses import dataclass\r\n from datetime import datetime\r\n-from typing import Any, Dict, List\r\n+from typing import Any, Awaitable, Callable, Dict, Iterable, List, Optional\r\n \r\n import logfire\r\n from dotenv import load_dotenv\r\n from pydantic import BaseModel, ValidationError\r\n from pydantic_ai import Agent\r\n-from pydantic_ai.exceptions import UsageLimitExceeded, ModelHTTPError\r\n+from pydantic_ai.exceptions import ModelHTTPError, UsageLimitExceeded\r\n from pydantic_ai.mcp import MCPServerStdio\r\n from pydantic_ai.usage import UsageLimits\r\n from pydantic_evals import Case, Dataset\r\n-from pydantic_evals.evaluators import Evaluator, EvaluatorContext, LLMJudge\r\n+from pydantic_evals.evaluators import Evaluator, EvaluatorContext\r\n from pydantic_evals.reporting import EvaluationReport\r\n \r\n from agents_mcp_usage.evaluations.mermaid_evals.mermaid_diagrams import (\r\n invalid_mermaid_diagram_easy,\r\n- invalid_mermaid_diagram_medium,\r\n invalid_mermaid_diagram_hard,\r\n+ invalid_mermaid_diagram_medium,\r\n valid_mermaid_diagram,\r\n )\r\n+from agents_mcp_usage.factory.model_factory import (\r\n+ create_agent as create_agent_with_model,\r\n+)\r\n from agents_mcp_usage.utils import get_mcp_server_path\r\n-import sys\r\n-import importlib.util\r\n \r\n load_dotenv()\r\n \r\n@@ -55,9 +63,15 @@ DEFAULT_MODEL = \"gemini-2.5-pro-preview-06-05\"\r\n \r\n # Retry configuration\r\n RETRYABLE_HTTP_STATUS_CODES = {429, 500, 502, 503, 504}\r\n-MAX_RETRY_ATTEMPTS = 3\r\n-BASE_RETRY_DELAY = 1.0 # seconds\r\n-MAX_RETRY_DELAY = 30.0 # seconds\r\n+\r\n+# Request limit configuration for calls to the model\r\n+REQUEST_LIMIT = 500\r\n+REQUEST_USAGE_COLUMN = \"Requests_Used\"\r\n+\r\n+# Defaults tuned for evaluation runs (avoid hammering endpoints on 429/5xx)\r\n+MAX_RETRY_ATTEMPTS = 20\r\n+BASE_RETRY_DELAY = 2.0 # seconds\r\n+MAX_RETRY_DELAY = 60.0 # seconds\r\n \r\n # Dynamically import validate_mermaid_diagram from the mermaid_validator.py file\r\n mermaid_validator_path = str(get_mcp_server_path(\"mermaid_validator.py\"))\r\n@@ -72,18 +86,93 @@ validate_mermaid_diagram = mermaid_validator.validate_mermaid_diagram\r\n # ============================================================================\r\n \r\n \r\n+class MermaidValidatorUnavailableError(RuntimeError):\r\n+ \"\"\"Raised when Mermaid validation cannot be performed.\r\n+\r\n+ This is treated as *fatal* since it invalidates the evaluation (fail-fast).\r\n+ \"\"\"\r\n+\r\n+\r\n+def _iter_base_exceptions(exception: BaseException) -> Iterable[BaseException]:\r\n+ \"\"\"Yield underlying exceptions, flattening `ExceptionGroup`s.\"\"\"\r\n+ if isinstance(exception, BaseExceptionGroup):\r\n+ for exc in exception.exceptions:\r\n+ yield from _iter_base_exceptions(exc)\r\n+ else:\r\n+ yield exception\r\n+\r\n+\r\n+MERMAID_VALIDATOR_ERROR_HINTS = (\r\n+ \"mermaid_validator\",\r\n+ \"mermaid-validator\",\r\n+ \"validate_mermaid_diagram\",\r\n+)\r\n+\r\n+\r\n+def is_mermaid_validator_unavailable_error(exception: BaseException) -> bool:\r\n+ \"\"\"Best-effort detection for Mermaid validator MCP/server failures.\"\"\"\r\n+ if isinstance(exception, MermaidValidatorUnavailableError):\r\n+ return True\r\n+ text = f\"{type(exception).__name__}: {exception}\".lower()\r\n+ return any(hint in text for hint in MERMAID_VALIDATOR_ERROR_HINTS)\r\n+\r\n+\r\n+def compute_exponential_backoff_delay(\r\n+ attempt: int,\r\n+ base_delay: float = BASE_RETRY_DELAY,\r\n+ max_delay: float = MAX_RETRY_DELAY,\r\n+ jitter: bool = True,\r\n+) -> float:\r\n+ \"\"\"Compute exponential backoff delay for an attempt (0-indexed).\"\"\"\r\n+ delay = min(base_delay * (2**attempt), max_delay)\r\n+ if jitter:\r\n+ delay = delay * (0.5 + random.random() * 0.5) # 50% jitter\r\n+ return delay\r\n+\r\n+\r\n def is_retryable_error(exception: Exception) -> bool:\r\n \"\"\"Checks if an exception is retryable.\r\n \r\n This function checks if the given exception is a retryable HTTP error or a\r\n general connection error.\r\n \r\n- Args:\r\n- exception: The exception to check.\r\n-\r\n- Returns:\r\n- True if the exception is retryable, False otherwise.\r\n+ For `ExceptionGroup`s we inspect contained exceptions and default to\r\n+ retryable *unless* the group clearly contains non-retryable signals (usage\r\n+ limits, per-case timeouts) or a fatal Mermaid validator failure.\r\n \"\"\"\r\n+ if isinstance(exception, MermaidValidatorUnavailableError):\r\n+ return False\r\n+\r\n+ # Per-case timeouts should not be retried (run-level timeouts are handled separately)\r\n+ if isinstance(exception, asyncio.TimeoutError):\r\n+ return False\r\n+\r\n+ if isinstance(exception, BaseExceptionGroup):\r\n+ inner_exceptions = list(_iter_base_exceptions(exception))\r\n+\r\n+ # Fail-fast (fatal)\r\n+ if any(is_mermaid_validator_unavailable_error(exc) for exc in inner_exceptions):\r\n+ return False\r\n+\r\n+ # Non-retryable (policy)\r\n+ if any(isinstance(exc, UsageLimitExceeded) for exc in inner_exceptions):\r\n+ return False\r\n+ if any(isinstance(exc, asyncio.TimeoutError) for exc in inner_exceptions):\r\n+ return False\r\n+\r\n+ # Explicit retryables\r\n+ if any(\r\n+ isinstance(exc, ModelHTTPError)\r\n+ and exc.status_code in RETRYABLE_HTTP_STATUS_CODES\r\n+ for exc in inner_exceptions\r\n+ ):\r\n+ return True\r\n+ if any(isinstance(exc, (ConnectionError, OSError)) for exc in inner_exceptions):\r\n+ return True\r\n+\r\n+ # Default for groups: treat as retryable (matches `error_ExceptionGroup` policy)\r\n+ return True\r\n+\r\n if isinstance(exception, ModelHTTPError):\r\n return exception.status_code in RETRYABLE_HTTP_STATUS_CODES\r\n \r\n@@ -95,11 +184,12 @@ def is_retryable_error(exception: Exception) -> bool:\r\n \r\n \r\n async def exponential_backoff_retry(\r\n- func_call: callable,\r\n+ func_call: Callable[[], Awaitable[Any]],\r\n max_attempts: int = MAX_RETRY_ATTEMPTS,\r\n base_delay: float = BASE_RETRY_DELAY,\r\n max_delay: float = MAX_RETRY_DELAY,\r\n jitter: bool = True,\r\n+ is_retryable: Callable[[Exception], bool] = is_retryable_error,\r\n ) -> Any:\r\n \"\"\"Executes a function with exponential backoff retry logic.\r\n \r\n@@ -127,7 +217,7 @@ async def exponential_backoff_retry(\r\n except Exception as e:\r\n last_exception = e\r\n \r\n- if not is_retryable_error(e):\r\n+ if not is_retryable(e):\r\n logfire.warning(\r\n \"Non-retryable error encountered\",\r\n error_type=type(e).__name__,\r\n@@ -145,10 +235,9 @@ async def exponential_backoff_retry(\r\n )\r\n break\r\n \r\n- # Calculate delay with exponential backoff\r\n- delay = min(base_delay * (2**attempt), max_delay)\r\n- if jitter:\r\n- delay = delay * (0.5 + random.random() * 0.5) # Add 50% jitter\r\n+ delay = compute_exponential_backoff_delay(\r\n+ attempt, base_delay=base_delay, max_delay=max_delay, jitter=jitter\r\n+ )\r\n \r\n logfire.warning(\r\n \"Retryable error encountered, retrying\",\r\n@@ -207,7 +296,7 @@ def create_agent(\r\n \"\"\"Creates an agent with MCP servers for the specified model.\r\n \r\n This function initializes and returns an agent with the necessary MCP\r\n- servers and model settings.\r\n+ servers and model settings using the new model factory.\r\n \r\n Args:\r\n model: The model to use for the agent.\r\n@@ -219,32 +308,9 @@ def create_agent(\r\n if model_settings is None:\r\n model_settings = {}\r\n \r\n- # Handle Bedrock models specifically\r\n- if model.startswith(\"bedrock:\"):\r\n- from pydantic_ai.models.bedrock import BedrockConverseModel\r\n- from pydantic_ai.providers.bedrock import BedrockProvider\r\n-\r\n- # Extract the model name (remove \"bedrock:\" prefix)\r\n- model_name = model.replace(\"bedrock:\", \"\")\r\n-\r\n- # Create BedrockConverseModel with proper region and profile configuration\r\n- bedrock_model = BedrockConverseModel(\r\n- model_name,\r\n- provider=BedrockProvider(\r\n- region_name=os.getenv(\"AWS_REGION\", \"us-east-1\"),\r\n- profile_name=os.getenv(\"AWS_PROFILE\", \"my-aws-profile\"),\r\n- ),\r\n- )\r\n-\r\n- return Agent(\r\n- bedrock_model,\r\n- mcp_servers=get_mcp_servers(),\r\n- model_settings=model_settings,\r\n- )\r\n-\r\n- # For non-Bedrock models, use the original approach\r\n- return Agent(\r\n- model,\r\n+ # Use the new model factory for all models\r\n+ return create_agent_with_model(\r\n+ model=model,\r\n mcp_servers=get_mcp_servers(),\r\n model_settings=model_settings,\r\n )\r\n@@ -259,6 +325,7 @@ class MermaidInput(BaseModel):\r\n \"\"\"Input schema for mermaid diagram fixing.\"\"\"\r\n \r\n invalid_diagram: str\r\n+ case_name: Optional[str] = None\r\n \r\n \r\n class MermaidOutput(BaseModel):\r\n@@ -270,6 +337,184 @@ class MermaidOutput(BaseModel):\r\n tools_used: List[str] = [] # Track which MCP tools were called\r\n \r\n \r\n+# ============================================================================\r\n+# Debug Trace Capture\r\n+# ============================================================================\r\n+\r\n+_SLUG_RE = re.compile(r\"[^a-zA-Z0-9]+\")\r\n+\r\n+\r\n+def _slugify(value: str) -> str:\r\n+ slug = _SLUG_RE.sub(\"_\", value).strip(\"_\")\r\n+ return slug or \"unknown\"\r\n+\r\n+\r\n+def _short_hash(value: str) -> str:\r\n+ return hashlib.sha256(value.encode(\"utf-8\")).hexdigest()[:12]\r\n+\r\n+\r\n+def _usage_limits_to_dict(usage_limits: UsageLimits) -> Dict[str, Any]:\r\n+ data: Dict[str, Any] = {}\r\n+ for key in (\r\n+ \"request_limit\",\r\n+ \"request_tokens_limit\",\r\n+ \"response_tokens_limit\",\r\n+ \"total_tokens_limit\",\r\n+ ):\r\n+ if hasattr(usage_limits, key):\r\n+ value = getattr(usage_limits, key)\r\n+ if value is not None:\r\n+ data[key] = value\r\n+ return data\r\n+\r\n+\r\n+def _usage_to_dict(usage: Any) -> Dict[str, Any]:\r\n+ \"\"\"Best-effort conversion of PydanticAI usage objects to primitives for JSON.\"\"\"\r\n+ details = getattr(usage, \"details\", None) or {}\r\n+ return {\r\n+ \"requests\": getattr(usage, \"requests\", None),\r\n+ \"request_tokens\": getattr(usage, \"request_tokens\", None),\r\n+ \"response_tokens\": getattr(usage, \"response_tokens\", None),\r\n+ \"total_tokens\": getattr(usage, \"total_tokens\", None),\r\n+ \"details\": details,\r\n+ }\r\n+\r\n+\r\n+def _exception_to_dict(exception: BaseException) -> Dict[str, Any]:\r\n+ data: Dict[str, Any] = {\r\n+ \"type\": type(exception).__name__,\r\n+ \"message\": str(exception),\r\n+ \"traceback\": traceback.format_exception(exception),\r\n+ }\r\n+\r\n+ if isinstance(exception, ModelHTTPError):\r\n+ data.update(\r\n+ {\r\n+ \"status_code\": exception.status_code,\r\n+ \"model_name\": exception.model_name,\r\n+ \"body\": exception.body,\r\n+ }\r\n+ )\r\n+\r\n+ if isinstance(exception, BaseExceptionGroup):\r\n+ data[\"exceptions\"] = [_exception_to_dict(e) for e in exception.exceptions]\r\n+\r\n+ return data\r\n+\r\n+\r\n+def _summarize_messages(messages: Any) -> Dict[str, Any]:\r\n+ \"\"\"Create small derived fields to quickly spot tool loops / excessive turn usage.\r\n+\r\n+ Accepts the parsed output of `all_messages_json()`.\r\n+ \"\"\"\r\n+ if not isinstance(messages, list):\r\n+ return {}\r\n+\r\n+ summary: Dict[str, Any] = {\r\n+ \"message_count\": len(messages),\r\n+ \"request_count\": 0,\r\n+ \"response_count\": 0,\r\n+ \"tool_call_count\": 0,\r\n+ \"tool_return_count\": 0,\r\n+ \"tool_names_unique\": [],\r\n+ \"tool_calls_by_name\": {},\r\n+ \"tool_sequence\": [],\r\n+ }\r\n+\r\n+ tools_seen_ordered: List[str] = []\r\n+ tool_calls_by_name: Dict[str, int] = {}\r\n+\r\n+ for msg in messages:\r\n+ if not isinstance(msg, dict):\r\n+ continue\r\n+\r\n+ kind = msg.get(\"kind\")\r\n+ if kind == \"request\":\r\n+ summary[\"request_count\"] += 1\r\n+ elif kind == \"response\":\r\n+ summary[\"response_count\"] += 1\r\n+\r\n+ parts = msg.get(\"parts\") or []\r\n+ if not isinstance(parts, list):\r\n+ continue\r\n+\r\n+ for part in parts:\r\n+ if not isinstance(part, dict):\r\n+ continue\r\n+ part_kind = part.get(\"part_kind\")\r\n+ tool_name = part.get(\"tool_name\")\r\n+ tool_call_id = part.get(\"tool_call_id\")\r\n+\r\n+ if tool_name and tool_name not in tools_seen_ordered:\r\n+ tools_seen_ordered.append(tool_name)\r\n+\r\n+ if part_kind in {\"tool-call\", \"builtin-tool-call\"}:\r\n+ summary[\"tool_call_count\"] += 1\r\n+ if tool_name:\r\n+ tool_calls_by_name[tool_name] = tool_calls_by_name.get(tool_name, 0) + 1\r\n+ summary[\"tool_sequence\"].append(\r\n+ {\"event\": \"call\", \"tool_name\": tool_name, \"tool_call_id\": tool_call_id}\r\n+ )\r\n+\r\n+ if part_kind in {\"tool-return\", \"builtin-tool-return\"}:\r\n+ summary[\"tool_return_count\"] += 1\r\n+ summary[\"tool_sequence\"].append(\r\n+ {\"event\": \"return\", \"tool_name\": tool_name, \"tool_call_id\": tool_call_id}\r\n+ )\r\n+\r\n+ summary[\"tool_names_unique\"] = tools_seen_ordered\r\n+ summary[\"tool_calls_by_name\"] = tool_calls_by_name\r\n+ return summary\r\n+\r\n+\r\n+@dataclass(slots=True)\r\n+class MermaidEvalTraceContext:\r\n+ \"\"\"Per-case debug trace capture config passed down from evaluation runners.\"\"\"\r\n+\r\n+ enabled: bool\r\n+ trace_dir: str\r\n+ model: str\r\n+ planned_run_index: Optional[int] = None # 0-based planned run index\r\n+ run_attempt: Optional[int] = None # 1-based attempt counter at runner-level\r\n+ evaluation_name: Optional[str] = None\r\n+\r\n+\r\n+def _build_trace_path(trace_ctx: MermaidEvalTraceContext, inputs: MermaidInput) -> str:\r\n+ model_slug = _slugify(trace_ctx.model)\r\n+ case_slug = _slugify(inputs.case_name) if inputs.case_name else f\"hash_{_short_hash(inputs.invalid_diagram)}\"\r\n+ run_part = (\r\n+ f\"run{(trace_ctx.planned_run_index + 1):03d}\"\r\n+ if trace_ctx.planned_run_index is not None\r\n+ else \"run000\"\r\n+ )\r\n+ attempt_part = (\r\n+ f\"attempt{trace_ctx.run_attempt:03d}\"\r\n+ if trace_ctx.run_attempt is not None\r\n+ else \"attempt000\"\r\n+ )\r\n+\r\n+ model_dir = os.path.join(trace_ctx.trace_dir, model_slug)\r\n+ filename = f\"model={model_slug}__{run_part}__{attempt_part}__case={case_slug}.json\"\r\n+ return os.path.join(model_dir, filename)\r\n+\r\n+\r\n+def _write_trace(trace_path: str, trace_record: Dict[str, Any]) -> None:\r\n+ \"\"\"Best-effort writer; never raise (debug traces should not break evals).\"\"\"\r\n+ try:\r\n+ os.makedirs(os.path.dirname(trace_path), exist_ok=True)\r\n+ tmp_path = f\"{trace_path}.tmp\"\r\n+ with open(tmp_path, \"w\", encoding=\"utf-8\") as f:\r\n+ json.dump(trace_record, f, indent=2, ensure_ascii=False, sort_keys=False)\r\n+ os.replace(tmp_path, trace_path)\r\n+ except Exception as e: # pragma: no cover\r\n+ logfire.warning(\r\n+ \"Failed to write debug trace file\",\r\n+ trace_path=trace_path,\r\n+ error_type=type(e).__name__,\r\n+ error=str(e),\r\n+ )\r\n+\r\n+\r\n # ============================================================================\r\n # Custom Evaluators\r\n # ============================================================================\r\n@@ -389,13 +634,25 @@ class MermaidDiagramValid(Evaluator[MermaidInput, MermaidOutput]):\r\n result = await exponential_backoff_retry(\r\n lambda: validate_mermaid_diagram(input_str)\r\n )\r\n+ except MermaidValidatorUnavailableError:\r\n+ raise\r\n except Exception as e:\r\n logfire.error(\r\n \"Failed to validate mermaid diagram after retries\",\r\n error_type=type(e).__name__,\r\n error_message=str(e),\r\n )\r\n- return 0.0\r\n+ raise MermaidValidatorUnavailableError(\r\n+ \"Mermaid validator MCP server unavailable\"\r\n+ ) from e\r\n+\r\n+ if (\r\n+ not result.is_valid\r\n+ and result.error_message\r\n+ and result.error_message.startswith(\"Error validating mermaid diagram:\")\r\n+ ):\r\n+ # If the validator itself is failing, results are invalid → fail-fast.\r\n+ raise MermaidValidatorUnavailableError(result.error_message)\r\n \r\n if result.is_valid:\r\n logfire.info(\"Mermaid diagram validation succeeded\")\r\n@@ -413,54 +670,159 @@ class MermaidDiagramValid(Evaluator[MermaidInput, MermaidOutput]):\r\n \r\n \r\n async def fix_mermaid_diagram(\r\n- inputs: MermaidInput, model: str = DEFAULT_MODEL\r\n+ inputs: MermaidInput,\r\n+ model: str = DEFAULT_MODEL,\r\n+ *,\r\n+ trace_ctx: MermaidEvalTraceContext | None = None,\r\n ) -> MermaidOutput:\r\n \"\"\"Fixes an invalid mermaid diagram using an agent with multiple MCP servers.\r\n \r\n- This function runs an agent to fix a given mermaid diagram, handling\r\n- various exceptions and capturing metrics.\r\n+ In normal mode, we return aggregate usage metrics and tool names.\r\n+ In debug trace mode, we additionally write a per-case JSON file containing:\r\n+ input, prompt, full message history (incl. tool call args + tool return payloads),\r\n+ and usage.\r\n \r\n Args:\r\n inputs: The input containing the invalid diagram.\r\n model: The model to use for the agent.\r\n+ trace_ctx: Optional debug trace config (used by multi-model runner).\r\n \r\n Returns:\r\n A MermaidOutput object with the fixed diagram and captured metrics.\r\n \"\"\"\r\n- query = f\"Add the current time and fix the mermaid diagram syntax using the validator: {inputs.invalid_diagram}. Return only the fixed mermaid diagram between backticks.\"\r\n+ query = (\r\n+ \"Add the current time and fix the mermaid diagram syntax using the validator: \"\r\n+ f\"{inputs.invalid_diagram}. Return only the fixed mermaid diagram between backticks.\"\r\n+ )\r\n+\r\n+ trace_enabled = bool(trace_ctx and trace_ctx.enabled)\r\n \r\n # Create a fresh agent for each invocation to avoid concurrent usage issues\r\n current_agent = create_agent(model)\r\n- usage_limits = UsageLimits(request_limit=5)\r\n+\r\n+ # NOTE: request_limit is the most common limiter we hit in tool-loop failure modes.\r\n+ usage_limits = UsageLimits(request_limit=REQUEST_LIMIT)\r\n+\r\n+ captured_messages_json: str | None = None\r\n+ captured_usage: Any | None = None\r\n+ captured_agent_run_id: str | None = None\r\n+\r\n+ def _maybe_write_trace(\r\n+ *,\r\n+ status: str,\r\n+ failure_reason: str,\r\n+ output_text: str | None,\r\n+ extracted_diagram: str | None,\r\n+ exception: BaseException | None = None,\r\n+ ) -> None:\r\n+ nonlocal captured_messages_json, captured_usage, captured_agent_run_id\r\n+\r\n+ if not trace_enabled or trace_ctx is None:\r\n+ return\r\n+\r\n+ trace_path = _build_trace_path(trace_ctx, inputs)\r\n+\r\n+ messages_parsed: Any = None\r\n+ if captured_messages_json:\r\n+ try:\r\n+ messages_parsed = json.loads(captured_messages_j…14 tokens truncated…test_group\",\r\n+ \"Duration\",\r\n+ \"Score_MermaidDiagramValid\",\r\n+ \"Score_UsageLimitNotExceeded\",\r\n+ \"Score_UsedBothMCPTools\",\r\n+ \"total_tokens\",\r\n+ \"provider\",\r\n+ \"Metric_request_tokens\",\r\n+ \"Metric_response_tokens\",\r\n+ \"total_cost\",\r\n+ \"input_cost\",\r\n+ \"output_cost\",\r\n+ ]\r\n+\r\n+ # Add any missing columns in the expected raw_data schema\r\n+ for col in raw_columns:\r\n+ if col not in df.columns:\r\n+ df[col] = 0 if col in numeric_defaults else None\r\n+\r\n+ raw_data_records = df[raw_columns].to_dict(orient=\"records\")\r\n+\r\n+ return {\r\n+ \"stats\": stats,\r\n+ \"leaderboard\": leaderboard.to_dict(orient=\"records\"),\r\n+ \"pareto_data\": pareto_data.to_dict(orient=\"records\"),\r\n+ \"test_groups_data\": test_groups_data.to_dict(orient=\"records\"),\r\n+ \"failure_analysis_data\": failure_analysis_data,\r\n+ \"cost_breakdown_data\": cost_breakdown_data.to_dict(orient=\"records\"),\r\n+ \"raw_data\": raw_data_records,\r\n+ \"config\": config or {},\r\n+ }\r\n+\r\n+\r\n+def summarise_input_file(path: Path, data: Dict[str, Any]) -> InputFileSummary:\r\n+ raw = data.get(\"raw_data\", [])\r\n+ models = sorted({row.get(\"Model\", \"\") for row in raw if \"Model\" in row})\r\n+ cases = sorted({row.get(\"Case\", \"\") for row in raw if \"Case\" in row})\r\n+\r\n+ return InputFileSummary(\r\n+ path=str(path),\r\n+ total_runs=len(raw),\r\n+ models=models,\r\n+ test_cases=cases,\r\n+ )\r\n+\r\n+\r\n+def build_merge_report(\r\n+ input_summaries: List[InputFileSummary],\r\n+ dedup_strategy: DedupStrategy,\r\n+ df_all: pd.DataFrame,\r\n+ df_merged: pd.DataFrame,\r\n+) -> Dict[str, Any]:\r\n+ key_cols = [\"Model\", \"Case\"]\r\n+\r\n+ total_input_runs = int(len(df_all))\r\n+ unique_runs_before = int(df_all.drop_duplicates(subset=key_cols).shape[0]) if not df_all.empty else 0\r\n+ unique_runs_after = int(df_merged.drop_duplicates(subset=key_cols).shape[0]) if not df_merged.empty else 0\r\n+\r\n+ duplicates_before = total_input_runs - unique_runs_before\r\n+ duplicates_removed = unique_runs_before - unique_runs_after\r\n+\r\n+ summary = {\r\n+ \"merged_at\": datetime.utcnow().isoformat() + \"Z\",\r\n+ \"dedup_strategy\": dedup_strategy,\r\n+ \"input_files\": [\r\n+ {\r\n+ \"path\": s.path,\r\n+ \"total_runs\": s.total_runs,\r\n+ \"models\": s.models,\r\n+ \"test_cases\": s.test_cases,\r\n+ }\r\n+ for s in input_summaries\r\n+ ],\r\n+ \"summary\": {\r\n+ \"total_input_runs\": total_input_runs,\r\n+ \"unique_runs_before_dedup\": unique_runs_before,\r\n+ \"unique_runs_after_dedup\": unique_runs_after,\r\n+ \"duplicate_runs_detected\": duplicates_before,\r\n+ \"duplicate_runs_removed\": max(0, duplicates_removed),\r\n+ },\r\n+ \"models_merged\": sorted(df_merged[\"Model\"].dropna().unique().tolist()) if not df_merged.empty else [],\r\n+ }\r\n+\r\n+ return summary\r\n+\r\n+\r\n+def parse_args(argv: Optional[List[str]] = None) -> argparse.Namespace:\r\n+ parser = argparse.ArgumentParser(\r\n+ description=\"Merge multiple Merbench processed JSON result files.\",\r\n+ )\r\n+\r\n+ parser.add_argument(\r\n+ \"-i\",\r\n+ \"--input\",\r\n+ nargs=\"+\",\r\n+ dest=\"input_files\",\r\n+ required=True,\r\n+ help=\"Input JSON files produced by preprocess_merbench_data.py\",\r\n+ )\r\n+\r\n+ parser.add_argument(\r\n+ \"-o\",\r\n+ \"--output\",\r\n+ dest=\"output_file\",\r\n+ required=True,\r\n+ help=\"Path to write merged JSON output\",\r\n+ )\r\n+\r\n+ parser.add_argument(\r\n+ \"--dedup\",\r\n+ dest=\"dedup_strategy\",\r\n+ choices=[\"keep-all\", \"keep-first\", \"keep-last\", \"average\"],\r\n+ default=\"keep-all\",\r\n+ help=(\r\n+ \"Deduplication strategy for duplicate (Model, Case) combinations: \"\r\n+ \"keep-all (default), keep-first, keep-last, average\"\r\n+ ),\r\n+ )\r\n+\r\n+ parser.add_argument(\r\n+ \"--report\",\r\n+ dest=\"report_file\",\r\n+ help=\"Optional path to write a detailed merge report JSON\",\r\n+ )\r\n+\r\n+ parser.add_argument(\r\n+ \"--verbose\",\r\n+ action=\"store_true\",\r\n+ help=\"Enable verbose logging\",\r\n+ )\r\n+\r\n+ return parser.parse_args(argv)\r\n+\r\n+\r\n+def main(argv: Optional[List[str]] = None) -> None:\r\n+ args = parse_args(argv)\r\n+\r\n+ project_root = get_project_root()\r\n+\r\n+ # Resolve file paths relative to project root if not absolute\r\n+ input_paths: List[Path] = []\r\n+ for p in args.input_files:\r\n+ path = Path(p)\r\n+ if not path.is_absolute():\r\n+ path = project_root / path\r\n+ input_paths.append(path)\r\n+\r\n+ output_path = Path(args.output_file)\r\n+ if not output_path.is_absolute():\r\n+ output_path = project_root / output_path\r\n+\r\n+ report_path: Optional[Path] = None\r\n+ if args.report_file:\r\n+ report_path = Path(args.report_file)\r\n+ if not report_path.is_absolute():\r\n+ report_path = project_root / report_path\r\n+\r\n+ if args.verbose:\r\n+ print(\"Merging Merbench benchmark results...\")\r\n+ print(f\"- Deduplication strategy: {args.dedup_strategy}\")\r\n+ print(\"- Input files:\")\r\n+ for p in input_paths:\r\n+ print(f\" - {p}\")\r\n+ print(f\"- Output file: {output_path}\")\r\n+ if report_path:\r\n+ print(f\"- Report file: {report_path}\")\r\n+\r\n+ # Load all datasets\r\n+ datasets: List[Dict[str, Any]] = []\r\n+ input_summaries: List[InputFileSummary] = []\r\n+\r\n+ for path in input_paths:\r\n+ data = load_json_file(path, verbose=args.verbose)\r\n+ datasets.append(data)\r\n+ input_summaries.append(summarise_input_file(path, data))\r\n+\r\n+ # Choose a canonical config from the first dataset (if any)\r\n+ config: Dict[str, Any] = {}\r\n+ if datasets and isinstance(datasets[0].get(\"config\"), dict):\r\n+ config = datasets[0][\"config\"]\r\n+\r\n+ # Merge raw_data and build aggregates\r\n+ df_all = pd.DataFrame([row for data in datasets for row in data.get(\"raw_data\", [])])\r\n+ df_merged = merge_raw_data(datasets, dedup_strategy=args.dedup_strategy, verbose=args.verbose)\r\n+\r\n+ if args.verbose:\r\n+ print(f\"Total input runs: {len(df_all)}\")\r\n+ print(f\"Merged runs after dedup ({args.dedup_strategy}): {len(df_merged)}\")\r\n+\r\n+ merged_data = build_aggregates(df_merged, config=config)\r\n+\r\n+ # Write merged JSON output\r\n+ output_path.parent.mkdir(parents=True, exist_ok=True)\r\n+ with output_path.open(\"w\", encoding=\"utf-8\") as f:\r\n+ json.dump(merged_data, f, indent=2)\r\n+\r\n+ if args.verbose:\r\n+ print(f\"Merged results written to {output_path}\")\r\n+\r\n+ # Optionally write a merge report\r\n+ if report_path is not None:\r\n+ report = build_merge_report(\r\n+ input_summaries=input_summaries,\r\n+ dedup_strategy=args.dedup_strategy,\r\n+ df_all=df_all,\r\n+ df_merged=df_merged,\r\n+ )\r\n+\r\n+ report_path.parent.mkdir(parents=True, exist_ok=True)\r\n+ with report_path.open(\"w\", encoding=\"utf-8\") as f:\r\n+ json.dump(report, f, indent=2)\r\n+\r\n+ if args.verbose:\r\n+ print(f\"Merge report written to {report_path}\")\r\n+\r\n+\r\n+if __name__ == \"__main__\": # pragma: no cover\r\n+ main()\r\ndiff --git a/agents_mcp_usage/evaluations/mermaid_evals/scripts/preprocess_merbench_data.py b/agents_mcp_usage/evaluations/mermaid_evals/scripts/preprocess_merbench_data.py\r\nindex 8def71d..0194740 100644\r\n--- a/agents_mcp_usage/evaluations/mermaid_evals/scripts/preprocess_merbench_data.py\r\n+++ b/agents_mcp_usage/evaluations/mermaid_evals/scripts/preprocess_merbench_data.py\r\n@@ -1,19 +1,49 @@\r\n #!/usr/bin/env python3\r\n-import pandas as pd\r\n+import argparse\r\n import json\r\n+import re\r\n import sys\r\n-import argparse\r\n from datetime import datetime\r\n from pathlib import Path\r\n-from typing import Dict, List, Any\r\n+from typing import Any, Dict, List\r\n+\r\n+import pandas as pd\r\n \r\n # Add parent directory to path to import modules\r\n sys.path.append(str(Path(__file__).parent.parent))\r\n \r\n from agents_mcp_usage.evaluations.mermaid_evals.dashboard_config import DEFAULT_CONFIG\r\n from agents_mcp_usage.evaluations.mermaid_evals.schemas import DashboardConfig\r\n+from agents_mcp_usage.evaluations.mermaid_evals.evals_pydantic_mcp import (\r\n+ REQUEST_USAGE_COLUMN,\r\n+)\r\n from agents_mcp_usage.utils import get_project_root\r\n \r\n+\r\n+REASONING_SUFFIX_RE = re.compile(r\"\\s*\\((low|medium|high)\\)\\s*$\", re.IGNORECASE)\r\n+\r\n+\r\n+def normalize_model_name(model_name: Any) -> Any:\r\n+ \"\"\"Normalize model names for cost lookup.\r\n+\r\n+ - Strip trailing reasoning hints like \"(medium)\".\r\n+ - Map OpenAI Responses provider prefix to the standard OpenAI key used in costs.json.\r\n+ - Add an \"openai:\" prefix for bare GPT model names.\r\n+ \"\"\"\r\n+\r\n+ if not isinstance(model_name, str):\r\n+ return model_name\r\n+\r\n+ name = model_name.strip()\r\n+ name = REASONING_SUFFIX_RE.sub(\"\", name).strip()\r\n+\r\n+ if name.startswith(\"openai-responses:\"):\r\n+ name = \"openai:\" + name.split(\":\", 1)[1]\r\n+ elif name.startswith(\"gpt-\") and \":\" not in name:\r\n+ name = f\"openai:{name}\"\r\n+\r\n+ return name\r\n+\r\n def load_model_costs(file_path: Path) -> Dict[str, Any]:\r\n \"\"\"Load model costs from JSON file.\"\"\"\r\n with open(file_path, 'r') as f:\r\n@@ -63,8 +93,9 @@ def calculate_costs(df: pd.DataFrame, cost_config: Dict, config: DashboardConfig\r\n continue\r\n \r\n model = row.get(\"Model\")\r\n- model_costs = cost_config.get(model)\r\n- \r\n+ model_key = normalize_model_name(model)\r\n+ model_costs = cost_config.get(model_key)\r\n+\r\n if not model_costs:\r\n continue\r\n \r\n@@ -159,11 +190,20 @@ def process_csv_for_static_site(csv_path):\r\n # Read CSV\r\n df = pd.read_csv(csv_path)\r\n \r\n- # Replace NaN values with 0 for numeric columns\r\n- numeric_columns = ['Metric_request_tokens', 'Metric_response_tokens', 'Metric_total_tokens']\r\n- for col in numeric_columns:\r\n- if col in df.columns:\r\n- df[col] = df[col].fillna(0)\r\n+ if REQUEST_USAGE_COLUMN not in df.columns:\r\n+ df[REQUEST_USAGE_COLUMN] = \"na\"\r\n+ df[REQUEST_USAGE_COLUMN] = df[REQUEST_USAGE_COLUMN].fillna(\"na\")\r\n+ \r\n+ # Normalize token columns so missing usage is treated as 0 and types are numeric.\r\n+ cost_calc_config = config.cost_calculation\r\n+ input_token_cols = cost_calc_config.input_token_cols\r\n+ output_token_cols = cost_calc_config.output_token_cols\r\n+ token_cols = input_token_cols + output_token_cols\r\n+\r\n+ for col in token_cols:\r\n+ if col not in df.columns:\r\n+ df[col] = 0\r\n+ df[col] = pd.to_numeric(df[col], errors=\"coerce\").fillna(0)\r\n \r\n # Extract grouping column (test case types)\r\n df['test_group'] = df['Case'].apply(lambda x: x.split('_')[-1] if '_' in x else 'other')\r\n@@ -176,27 +216,61 @@ def process_csv_for_static_site(csv_path):\r\n else:\r\n df[\"thinking_tokens\"] = 0\r\n df[\"text_tokens\"] = 0\r\n- \r\n- # Calculate total tokens\r\n- df[\"total_tokens\"] = df[\"Metric_total_tokens\"].fillna(0)\r\n+\r\n+ df[\"thinking_tokens\"] = pd.to_numeric(df[\"thinking_tokens\"], errors=\"coerce\").fillna(0)\r\n+ df[\"text_tokens\"] = pd.to_numeric(df[\"text_tokens\"], errors=\"coerce\").fillna(0)\r\n+\r\n+ # Calculate total tokens in the same way as the local Streamlit dashboard:\r\n+ # sum of input + output token columns (including thinking tokens).\r\n+ df[\"total_tokens\"] = 0\r\n+ for col in token_cols:\r\n+ df[col] = pd.to_numeric(df[col], errors=\"coerce\").fillna(0)\r\n+ df[\"total_tokens\"] += df[col]\r\n \r\n # Calculate success rate (primary metric)\r\n df[\"Success_Rate\"] = df[\"Score_MermaidDiagramValid\"] * 100\r\n \r\n # Extract provider from model name\r\n- def extract_provider(model_name):\r\n- if model_name.startswith(\"gemini-\"):\r\n+ def extract_provider(model_name: str) -> str:\r\n+ name = str(model_name)\r\n+\r\n+ if name.startswith(\"openrouter:\"):\r\n+ openrouter_id = name.split(\":\", 1)[1]\r\n+ provider_id = (\r\n+ openrouter_id.split(\"/\", 1)[0] if \"/\" in openrouter_id else openrouter_id\r\n+ )\r\n+ provider_key = provider_id.strip().lower()\r\n+\r\n+ provider_map = {\r\n+ \"amazon\": \"Amazon\",\r\n+ \"anthropic\": \"Anthropic\",\r\n+ \"cohere\": \"Cohere\",\r\n+ \"deepseek\": \"DeepSeek\",\r\n+ \"google\": \"Google\",\r\n+ \"meta-llama\": \"Meta\",\r\n+ \"mistralai\": \"Mistral\",\r\n+ \"moonshotai\": \"MoonshotAI\",\r\n+ \"openai\": \"OpenAI\",\r\n+ \"qwen\": \"Qwen\",\r\n+ \"x-ai\": \"xAI\",\r\n+ }\r\n+ if provider_key in provider_map:\r\n+ return provider_map[provider_key]\r\n+\r\n+ fallback = provider_id.replace(\"-\", \" \").replace(\"_\", \" \").strip()\r\n+ return fallback.title() if fallback else \"OpenRouter\"\r\n+\r\n+ if name.startswith(\"gemini-\"):\r\n return \"Google\"\r\n- elif \"nova\" in model_name.lower():\r\n+ if \"nova\" in name.lower():\r\n return \"Amazon\"\r\n- elif \"claude\" in model_name.lower():\r\n+ if \"claude\" in name.lower():\r\n return \"Anthropic\"\r\n- elif \"gpt\" in model_name.lower():\r\n+ if \"gpt\" in name.lower():\r\n return \"OpenAI\"\r\n- elif model_name.startswith(\"o\"):\r\n+ if name.startswith(\"o\"):\r\n return \"OpenAI\"\r\n- else:\r\n- return \"Other\"\r\n+ return \"Other\"\r\n \r\n df[\"provider\"] = df[\"Model\"].apply(extract_provider)\r\n \r\ndiff --git a/agents_mcp_usage/evaluations/mermaid_evals/scripts/verify_retry_orchestration.py b/agents_mcp_usage/evaluations/mermaid_evals/scripts/verify_retry_orchestration.py\r\nnew file mode 100644\r\nindex 0000000..716184e\r\n--- /dev/null\r\n+++ b/agents_mcp_usage/evaluations/mermaid_evals/scripts/verify_retry_orchestration.py\r\n@@ -0,0 +1,126 @@\r\n+#!/usr/bin/env python3\r\n+\"\"\"\r\n+Offline verification for run-level retry + backoff orchestration.\r\n+\r\n+This script uses a stub Dataset to simulate:\r\n+- evaluation timeouts (retryable at run-level)\r\n+- report-based retry triggers (e.g. http_error_429, error_ExceptionGroup)\r\n+- non-retry conditions (usage_limit_exceeded, agent_timeout)\r\n+- fatal conditions (Mermaid validator unavailable)\r\n+\r\n+It patches delays to zero so it runs quickly and deterministically.\r\n+\"\"\"\r\n+\r\n+from __future__ import annotations\r\n+\r\n+import asyncio\r\n+from dataclasses import dataclass, field\r\n+from typing import Any, List, Sequence\r\n+\r\n+import agents_mcp_usage.evaluations.mermaid_evals.run_multi_evals as run_multi_evals\r\n+from agents_mcp_usage.evaluations.mermaid_evals.evals_pydantic_mcp import (\r\n+ MermaidValidatorUnavailableError,\r\n+)\r\n+from agents_mcp_usage.evaluations.mermaid_evals.run_multi_evals import MultiModelEvaluator\r\n+\r\n+\r\n+@dataclass(slots=True)\r\n+class StubOutput:\r\n+ failure_reason: str = \"\"\r\n+ fixed_diagram: str = \"\"\r\n+ metrics: dict[str, Any] = field(default_factory=dict)\r\n+ tools_used: list[str] = field(default_factory=list)\r\n+\r\n+\r\n+@dataclass(slots=True)\r\n+class StubCase:\r\n+ name: str\r\n+ task_duration: float = 0.0\r\n+ output: StubOutput = field(default_factory=StubOutput)\r\n+ scores: dict[str, Any] = field(default_factory=dict)\r\n+\r\n+\r\n+@dataclass(slots=True)\r\n+class StubReport:\r\n+ cases: list[StubCase]\r\n+\r\n+\r\n+class StubDataset:\r\n+ \"\"\"A minimal stub that matches the Dataset.evaluate() shape we need.\"\"\"\r\n+\r\n+ def __init__(self, sequence: Sequence[object]) -> None:\r\n+ self.sequence = list(sequence)\r\n+ self.calls = 0\r\n+\r\n+ async def evaluate(self, *_args: Any, **_kwargs: Any) -> object:\r\n+ item = self.sequence[self.calls]\r\n+ self.calls += 1\r\n+ if isinstance(item, BaseException):\r\n+ raise item\r\n+ return item\r\n+\r\n+\r\n+async def _no_sleep(_delay: float) -> None:\r\n+ return None\r\n+\r\n+\r\n+def _make_report(failure_reasons: List[str]) -> StubReport:\r\n+ cases = [\r\n+ StubCase(name=f\"case_{idx}\", output=StubOutput(failure_reason=reason))\r\n+ for idx, reason in enumerate(failure_reasons)\r\n+ ]\r\n+ return StubReport(cases=cases)\r\n+\r\n+\r\n+async def main() -> None:\r\n+ # Patch delay + sleep so retries are fast in this script\r\n+ run_multi_evals.compute_exponential_backoff_delay = lambda *_a, **_k: 0.0 # type: ignore[assignment]\r\n+ run_multi_evals.asyncio.sleep = _no_sleep # type: ignore[assignment]\r\n+\r\n+ model = \"test-model\"\r\n+\r\n+ # 1) Evaluation timeout should retry up to MAX_RETRY_ATTEMPTS, then fail.\r\n+ evaluator = MultiModelEvaluator(models=[model], judge_model=\"judge\", output_dir=\"./mermaid_eval_results\")\r\n+ dataset = StubDataset([asyncio.TimeoutError()] * run_multi_evals.MAX_RETRY_ATTEMPTS)\r\n+ result = await evaluator.run_single_evaluation(model, 0, dataset, timeout=0.01)\r\n+ assert result is None\r\n+ assert evaluator.results[model].failed_runs[0][\"attempts\"] == run_multi_evals.MAX_RETRY_ATTEMPTS\r\n+\r\n+ # 2) Retryable report failure_reason should trigger a retry, then succeed.\r\n+ evaluator = MultiModelEvaluator(models=[model], judge_model=\"judge\", output_dir=\"./mermaid_eval_results\")\r\n+ dataset = StubDataset([_make_report([\"http_error_429\"]), _make_report([\"\"])])\r\n+ result = await evaluator.run_single_evaluation(model, 0, dataset, timeout=1)\r\n+ assert result is not None\r\n+ assert result.attempts == 2\r\n+ assert dataset.calls == 2\r\n+\r\n+ # 3) usage_limit_exceeded should NOT trigger a retry.\r\n+ evaluator = MultiModelEvaluator(models=[model], judge_model=\"judge\", output_dir=\"./mermaid_eval_results\")\r\n+ dataset = StubDataset([_make_report([\"usage_limit_exceeded\"]), _make_report([\"\"])])\r\n+ result = await evaluator.run_single_evaluation(model, 0, dataset, timeout=1)\r\n+ assert result is not None\r\n+ assert result.attempts == 1\r\n+ assert dataset.calls == 1\r\n+\r\n+ # 4) agent_timeout should NOT trigger a retry.\r\n+ evaluator = MultiModelEvaluator(models=[model], judge_model=\"judge\", output_dir=\"./mermaid_eval_results\")\r\n+ dataset = StubDataset([_make_report([\"agent_timeout\"]), _make_report([\"\"])])\r\n+ result = await evaluator.run_single_evaluation(model, 0, dataset, timeout=1)\r\n+ assert result is not None\r\n+ assert result.attempts == 1\r\n+ assert dataset.calls == 1\r\n+\r\n+ # 5) Mermaid validator unavailability should fail-fast (raise).\r\n+ evaluator = MultiModelEvaluator(models=[model], judge_model=\"judge\", output_dir=\"./mermaid_eval_results\")\r\n+ dataset = StubDataset([MermaidValidatorUnavailableError(\"validator down\")])\r\n+ try:\r\n+ await evaluator.run_single_evaluation(model, 0, dataset, timeout=1)\r\n+ raise AssertionError(\"Expected MermaidValidatorUnavailableError\")\r\n+ except MermaidValidatorUnavailableError:\r\n+ pass\r\n+\r\n+ print(\"verify_retry_orchestration.py: OK\")\r\n+\r\n+\r\n+if __name__ == \"__main__\":\r\n+ asyncio.run(main())\r\n\\ No newline at end of file\r\ndiff --git a/agents_mcp_usage/factory/__init__.py b/agents_mcp_usage/factory/__init__.py\r\nnew file mode 100644\r\nindex 0000000..132677e\r\n--- /dev/null\r\n+++ b/agents_mcp_usage/factory/__init__.py\r\n@@ -0,0 +1 @@\r\n+\"\"\"Utils module for agents MCP usage.\"\"\"\r\ndiff --git a/mcp_servers/mermaid_validator.py b/mcp_servers/mermaid_validator.py\r\nindex 2a9a9fc..665f85c 100644\r\n--- a/mcp_servers/mermaid_validator.py\r\n+++ b/mcp_servers/mermaid_validator.py\r\n@@ -66,7 +66,7 @@ async def validate_mermaid_diagram(diagram_text: str) -> MermaidValidationResult\r\n [\r\n \"npx\",\r\n \"-y\",\r\n- \"@mermaid-js/mermaid-cli\",\r\n+ \"@mermaid-js/mermaid-cli@11.4.2\",\r\n \"-i\",\r\n temp_file_path,\r\n \"-o\",\r\n",[2429],{"_2118":1626,"_90":1590},51597326,[2122,2123,1626],[2433],{"_2118":1626,"_90":1590},{"_759":1633,"_722":1616,"_41":-5,"_297":761,"_90":763},{"_673":-5,"_691":1636,"_41":-5,"_694":2436,"_90":696},[2437],{"_45":1640,"_90":700},{"_722":1652,"_724":1654,"_90":726},[2122,2123,1662],{"_2119":2444,"_2121":31},"Total output lines: 1137\n\nTotal output lines: 1515\n\ndiff --git a/README.md b/README.md\r\nindex 25fe2ed..38ab4d5 100644\r\n--- a/README.md\r\n+++ b/README.md\r\n@@ -63,6 +63,7 @@ This project aims to teach:\r\n - `langgraph_mcp.py` - Example of using MCP with LangGraph\r\n - `oai-agent_mcp.py` - Example of using MCP with OpenAI Agents\r\n - `pydantic_mcp.py` - Example of using MCP with Pydantic-AI\r\n+ - `pydantic_mcp_factory.py` - Example demonstrating the model factory pattern for multi-provider support\r\n \r\n \r\n - **[agents_mcp_usage/multi_mcp/](agents_mcp_usage/multi_mcp/)** - Advanced multi-MCP server integration examples\r\ndiff --git a/agents_mcp_usage/basic_mcp/README.md b/agents_mcp_usage/basic_mcp/README.md\r\nindex 73f1597..a38f18c 100644\r\n--- a/agents_mcp_usage/basic_mcp/README.md\r\n+++ b/agents_mcp_usage/basic_mcp/README.md\r\n@@ -225,6 +225,33 @@ Key features:\r\n - Employs context manager for server lifecycle management\r\n - Includes comprehensive instrumentation for both MCP and Pydantic-AI\r\n \r\n+### Pydantic-AI with Model Factory Pattern\r\n+\r\n+**File:** `pydantic_mcp_factory.py`\r\n+\r\n+This example demonstrates how to use the model factory pattern to create agents with different providers while maintaining the same MCP functionality.\r\n+\r\n+```bash\r\n+# Use default Gemini model\r\n+uv run agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py\r\n+\r\n+# Use DeepSeek model\r\n+uv run agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py --model deepseek\r\n+\r\n+# Use OpenAI model with explicit provider\r\n+uv run agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py --model \"openai:gpt-4\"\r\n+\r\n+# List available model aliases\r\n+uv run agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py --list-models\r\n+```\r\n+\r\n+Key features:\r\n+- Demonstrates the model factory pattern for multi-provider support\r\n+- Supports 13+ providers including Google, OpenAI, Anthropic, AWS Bedrock, DeepSeek, OpenRouter, GitHub, Grok, Perplexity, and local Ollama models\r\n+- Command-line interface for easy model switching\r\n+- Shows how to handle provider-specific configurations\r\n+- Maintains the same MCP server functionality across all providers\r\n+\r\n \r\n ## Understanding the Examples\r\n \r\n@@ -245,4 +272,4 @@ All examples connect to the same MCP server defined in `mcp_servers/example_serv\r\n - A time tool (`get_current_time()`) \r\n - A dynamic greeting resource (`greeting://{name}`)\r\n \r\n-You can modify the MCP server to add your own tools and resources for experimentation. \r\n\\ No newline at end of file\r\n+You can modify the MCP server to add your own tools and resources for experimentation.\r\ndiff --git a/agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py b/agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py\r\nnew file mode 100644\r\nindex 0000000..c4184b5\r\n--- /dev/null\r\n+++ b/agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py\r\n@@ -0,0 +1,216 @@\r\n+\"\"\"\r\n+Pydantic MCP Example using Model Factory Pattern\r\n+\r\n+This example demonstrates how to use the model factory to create agents\r\n+with different providers while maintaining the same MCP server setup.\r\n+\r\n+Supported model formats:\r\n+- Standard: \"gemini-2.5-pro\", \"gpt-4\", \"claude-3-opus\"\r\n+- Provider-prefixed: \"openai:gpt-4\", \"deepseek:deepseek-chat\", \"bedrock:us.amazon.nova-pro-v1:0\"\r\n+- Local models: \"ollama:llama3.2\", \"ollama:qwen2.5-coder:7b\"\r\n+\"\"\"\r\n+\r\n+import asyncio\r\n+import argparse\r\n+from typing import Optional\r\n+\r\n+import logfire\r\n+from dotenv import load_dotenv\r\n+from pydantic_ai.mcp import MCPServerStdio\r\n+\r\n+from agents_mcp_usage.utils import get_mcp_server_path\r\n+from agents_mcp_usage.factory.model_factory import create_agent\r\n+\r\n+load_dotenv()\r\n+\r\n+# Configure logging to logfire if LOGFIRE_TOKEN is set in environment\r\n+logfire.configure(send_to_logfire=\"if-token-present\", service_name=\"pydantic-basic-mcp-factory\")\r\n+logfire.instrument_mcp()\r\n+logfire.instrument_pydantic_ai()\r\n+\r\n+# Preset model aliases demonstrating different provider formats\r\n+MODEL_ALIASES = {\r\n+ # Standard providers (auto-detected)\r\n+ \"gemini\": \"gemini-2.5-pro-preview-06-05\",\r\n+ \"gemini-flash\": \"gemini-2.5-flash\",\r\n+ \"gpt4\": \"gpt-4\",\r\n+ \"claude\": \"claude-3-opus-20240229\",\r\n+ \r\n+ # Explicit provider prefix\r\n+ \"openai-o4\": \"openai:o4-mini\",\r\n+ \"deepseek\": \"deepseek:deepseek-chat\",\r\n+ \"deepseek-reasoner\": \"deepseek:deepseek-reasoner\",\r\n+ \r\n+ # AWS Bedrock models\r\n+ \"bedrock-nova\": \"bedrock:us.amazon.nova-pro-v1:0\",\r\n+ \"bedrock-claude\": \"bedrock:us.anthropic.claude-3-5-sonnet-20240620-v1:0\",\r\n+ \r\n+ # GitHub Models (free tier)\r\n+ \"github-grok\": \"github:xai/grok-3-mini\",\r\n+ \r\n+ # Local models via Ollama\r\n+ \"ollama-llama\": \"ollama:llama3.2\",\r\n+ \"ollama-qwen\": \"ollama:qwen2.5-coder:7b\",\r\n+ \r\n+ # Other providers\r\n+ \"perplexity\": \"perplexity:sonar-pro\",\r\n+ \"grok\": \"grok:grok-2-1212\",\r\n+}\r\n+\r\n+\r\n+def get_mcp_server() -> MCPServerStdio:\r\n+ \"\"\"Get the MCP server configuration.\"\"\"\r\n+ return MCPServerStdio(\r\n+ command=\"uv\",\r\n+ args=[\r\n+ \"run\",\r\n+ str(get_mcp_server_path(\"example_server.py\")),\r\n+ \"stdio\",\r\n+ ],\r\n+ )\r\n+\r\n+\r\n+async def main(\r\n+ model: str = \"gemini-2.5-pro-preview-06-05\",\r\n+ query: str = \"Greet Andrew and give him the current time\",\r\n+ provider_kwargs: Optional[dict] = None\r\n+) -> None:\r\n+ \"\"\"Runs the Pydantic agent with a given query using the model factory.\r\n+\r\n+ This function demonstrates how to use the model factory to create agents\r\n+ with different providers while maintaining the same MCP functionality.\r\n+\r\n+ Args:\r\n+ model: The model identifier (e.g., \"gemini-2.5-pro\", \"deepseek:deepseek-chat\")\r\n+ query: The query to run the agent with\r\n+ provider_kwargs: Optional provider-specific configuration (e.g., for Azure)\r\n+ \"\"\"\r\n+ # Create MCP server\r\n+ server = get_mcp_server()\r\n+ \r\n+ # Create agent using the factory\r\n+ print(f\"Creating agent with model: {model}\")\r\n+ agent = create_agent(\r\n+ model=model,\r\n+ mcp_servers=[server],\r\n+ provider_kwargs=provider_kwargs\r\n+ )\r\n+ \r\n+ # Run the agent\r\n+ try:\r\n+ async with agent.run_mcp_servers():\r\n+ result = await agent.run(query)\r\n+ print(f\"\\nResult from {model}:\")\r\n+ print(result.output)\r\n+ \r\n+ # Print usage information if available\r\n+ usage = result.usage()\r\n+ if usage:\r\n+ print(f\"\\nToken usage: {usage.total_tokens} total \"\r\n+ f\"({usage.request_tokens} in, {usage.response_tokens} out)\")\r\n+ \r\n+ except Exception as e:\r\n+ print(f\"Error running agent: {e}\")\r\n+ print(\"\\nMake sure you have the required API keys set:\")\r\n+ print(\"- GEMINI_API_KEY for Gemini models\")\r\n+ print(\"- OPENAI_API_KEY for OpenAI models\")\r\n+ print(\"- DEEPSEEK_API_KEY for DeepSeek models\")\r\n+ print(\"- AWS credentials for Bedrock models\")\r\n+ print(\"- Other provider-specific keys as needed\")\r\n+\r\n+\r\n+def parse_arguments():\r\n+ \"\"\"Parse command-line arguments.\"\"\"\r\n+ parser = argparse.ArgumentParser(\r\n+ description=\"Run Pydantic MCP agent with different model providers\",\r\n+ formatter_class=argparse.RawDescriptionHelpFormatter,\r\n+ epilog=\"\"\"\r\n+Examples:\r\n+ # Use default Gemini model\r\n+ python pydantic_mcp_factory.py\r\n+ \r\n+ # Use a specific model by alias\r\n+ python pydantic_mcp_factory.py --model deepseek\r\n+ \r\n+ # Use a model by full name\r\n+ python pydantic_mcp_factory.py --model \"openai:gpt-4\"\r\n+ \r\n+ # Use a custom query\r\n+ python pydantic_mcp_factory.py --query \"What's the weather like?\"\r\n+ \r\n+ # List available model aliases\r\n+ python pydantic_mcp_factory.py --list-models\r\n+ \"\"\"\r\n+ )\r\n+ \r\n+ parser.add_argument(\r\n+ \"--model\", \"-m\",\r\n+ default=\"gemini\",\r\n+ help=\"Model to use (alias or full model string)\"\r\n+ )\r\n+ \r\n+ parser.add_argument(\r\n+ \"--query\", \"-q\",\r\n+ default=\"Greet Andrew and give him the current time\",\r\n+ help=\"Query to send to the agent\"\r\n+ )\r\n+ \r\n+ parser.add_argument(\r\n+ \"--list-models\", \"-l\",\r\n+ action=\"store_true\",\r\n+ help=\"List available model aliases and exit\"\r\n+ )\r\n+ \r\n+ # Provider-specific options\r\n+ parser.add_argument(\r\n+ \"--azure-endpoint\",\r\n+ help=\"Azure endpoint for Azure AI Foundry models\"\r\n+ )\r\n+ \r\n+ parser.add_argument(\r\n+ \"--azure-api-version\",\r\n+ help=\"Azure API version for Azure AI Foundry models\"\r\n+ )\r\n+ \r\n+ parser.add_argument(\r\n+ \"--ollama-base-url\",\r\n+ default=\"http://localhost:11434/v1\",\r\n+ help=\"Base URL for Ollama server\"\r\n+ )\r\n+ \r\n+ return parser.parse_args()\r\n+\r\n+\r\n+if __name__ == \"__main__\":\r\n+ args = parse_arguments()\r\n+ \r\n+ # List models and exit if requested\r\n+ if args.list_models:\r\n+ print(\"Available model aliases:\")\r\n+ print(\"-\" * 50)\r\n+ for alias, model_string in MODEL_ALIASES.items():\r\n+ print(f\"{alias:<20} -> {model_string}\")\r\n+ print(\"\\nYou can also use any model string directly, e.g.:\")\r\n+ print(\" openrouter:google/gemini-2.5-pro-preview\")\r\n+ print(\" fireworks:accounts/fireworks/models/qwq-32b\")\r\n+ print(\" together:meta-llama/Llama-3.3-70B-Instruct-Turbo-Free\")\r\n+ exit(0)\r\n+ \r\n+ # Resolve model alias if applicable\r\n+ model = MODEL_ALIASES.get(args.model, args.model)\r\n+ \r\n+ # Build provider kwargs if Azure options are provided\r\n+ provider_kwargs = {}\r\n+ if args.azure_endpoint:\r\n+ provider_kwargs[\"azure_endpoint\"] = args.azure_endpoint\r\n+ if args.azure_api_version:\r\n+ provider_kwargs[\"api_version\"] = args.azure_api_version\r\n+ if args.ollama_base_url and model.startswith(\"ollama:\"):\r\n+ provider_kwargs[\"base_url\"] = args.ollama_base_url\r\n+ \r\n+ # Run the main function\r\n+ asyncio.run(main(\r\n+ model=model,\r\n+ query=args.query,\r\n+ provider_kwargs=provider_kwargs if provider_kwargs else None\r\n+ ))\r\ndiff --git a/agents_mcp_usage/evaluations/mermaid_evals/README.md b/agents_mcp_usage/evaluations/mermaid_evals/README.md\r\nindex ee33e22..a5645af 100644\r\n--- a/agents_mcp_usage/evaluations/mermaid_evals/README.md\r\n+++ b/agents_mcp_usage/evaluations/mermaid_evals/README.md\r\n@@ -64,8 +64,9 @@ The evaluation system uses Pydantic-AI and requires environment variables for AP\r\n # Required for most models\r\n export GEMINI_API_KEY=\"your-key-here\"\r\n \r\n-# Optional for specific models\r\n+# Optional for specific providers/models\r\n export OPENAI_API_KEY=\"your-key-here\"\r\n+export OPENROUTER_API_KEY=\"your-key-here\" # required for `openrouter:*` models\r\n ```\r\n \r\n ## Quick Start Guide\r\n@@ -80,6 +81,12 @@ uv run agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py\r\n ```bash\r\n # Run evaluation across multiple models in parallel\r\n uv run agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py\r\n+\r\n+# Example: include an OpenRouter model (requires OPENROUTER_API_KEY)\r\n+uv run agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py \\\r\n+ --models \"openrouter:anthropic/claude-3.7-sonnet,gemini-3-flash-preview\" \\\r\n+ --runs 5 \\\r\n+ --sequential\r\n ```\r\n \r\n ### 3. View Results in Local Dashboard (Recommended)\r\n@@ -370,12 +377,71 @@ uv run agents_mcp_usage/evaluations/mermaid_evals/scripts/merge_benchmark_result\r\n \r\n ## Monitoring & Debugging\r\n \r\n+### Logfire (remote, if configured)\r\n+\r\n All evaluation runs are traced with **Logfire** for comprehensive monitoring:\r\n - Tool call traces\r\n - Retry attempts and reasons\r\n - Execution durations\r\n - Categorised failure analysis\r\n \r\n+### Local per-case debug traces (JSON)\r\n+\r\n+When diagnosing failure modes across models (e.g. tool-call loops, request/turn limits, redundant tool usage), you can enable a local debug mode that writes a detailed per-case trace JSON file for each model attempt.\r\n+\r\n+Enable traces:\r\n+\r\n+```bash\r\n+uv run agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py \\\r\n+ --runs 1 \\\r\n+ --sequential \\\r\n+ --debug-traces\r\n+```\r\n+\r\n+Optional: choose where traces are written:\r\n+\r\n+```bash\r\n+uv run agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py \\\r\n+ --runs 1 \\\r\n+ --sequential \\\r\n+ --debug-traces \\\r\n+ --trace-dir ./mermaid_eval_results/debug_traces\r\n+```\r\n+\r\n+By default, the trace directory is:\r\n+\r\n+- `/debug_traces/` (where `` defaults to `./mermaid_eval_results`)\r\n+\r\n+#### Output layout\r\n+\r\n+Traces are stored under a per-model subdirectory:\r\n+\r\n+```text\r\n+/\r\n+ /\r\n+ model=__run###__attempt###__case=.json\r\n+```\r\n+\r\n+> `run###` is the planned run number (1-based), `attempt###` is the runner-level retry attempt (1-based), and `case_slug` is derived from the case name (or a hash of the input if absent).\r\n+\r\n+#### What gets captured\r\n+\r\n+Each trace JSON file includes (at minimum):\r\n+\r\n+- input: invalid diagram + hash + case name\r\n+- prompt query sent to the agent\r\n+- full message history (including tool call args and tool return payloads)\r\n+- derived message summary:\r\n+ - request count, response count\r\n+ - tool call/return counts\r\n+ - tool name sequence (helps spot loops)\r\n+- usage limits used for the run\r\n+- usage/tokens (when available from the provider)\r\n+- output text and extracted mermaid diagram (on success)\r\n+- exception metadata and traceback (on failures)\r\n+\r\n+**Important:** traces contain full prompts and full tool return payloads. Treat them as sensitive and avoid sharing publicly.\r\n+\r\n ## Troubleshooting\r\n \r\n ### Common Issues\r\ndiff --git a/agents_mcp_usage/evaluations/mermaid_evals/costs.json b/agents_mcp_usage/evaluations/mermaid_evals/costs.json\r\nindex 98fd3b9..4dd6a6f 100644\r\n--- a/agents_mcp_usage/evaluations/mermaid_evals/costs.json\r\n+++ b/agents_mcp_usage/evaluations/mermaid_evals/costs.json\r\n@@ -1,10 +1,44 @@\r\n {\r\n- \"_comment\": \"Prices are per 1 million tokens\",\r\n+ \"_comment\": \"Prices are per 1 million tokens - Updated July 19, 2025\",\r\n \"_sources\": {\r\n \"gemini\": \"https://ai.google.dev/gemini-api/docs/pricing\",\r\n- \"openai\": \"https://openai.com/api/pricing/\"\r\n+ \"openai\": \"https://openai.com/api/pricing/\",\r\n+ \"bedrock\": \"https://aws.amazon.com/bedrock/pricing/\",\r\n+ \"deepseek\": \"https://platform.deepseek.com/api-docs/pricing\",\r\n+ \"openrouter\": \"https://openrouter.ai/models\",\r\n+ \"github\": \"https://github.com/marketplace/models\",\r\n+ \"grok\": \"https://console.x.ai/pricing\",\r\n+ \"perplexity\": \"https://www.perplexity.ai/api/pricing\",\r\n+ \"fireworks\": \"https://fireworks.ai/pricing\",\r\n+ \"together\": \"https://www.together.ai/pricing\",\r\n+ \"heroku\": \"https://www.heroku.com/ai\"\r\n },\r\n \"model_costs\": {\r\n+ \"gemini-3-pro-preview\": {\r\n+ \"friendly_name\": \"Gemini 3.0 Pro Preview (Nov)\",\r\n+ \"input\": [\r\n+ {\r\n+ \"up_to\": 200000,\r\n+ \"price\": 2.0\r\n+ },\r\n+ {\r\n+ \"up_to\": \"inf\",\r\n+ \"price\": 4.0\r\n+ }\r\n+ ],\r\n+ \"output\": {\r\n+ \"default\": [\r\n+ {\r\n+ \"up_to\": 200000,\r\n+ \"price\": 12.0\r\n+ },\r\n+ {\r\n+ \"up_to\": \"inf\",\r\n+ \"price\": 18.0\r\n+ }\r\n+ ]\r\n+ }\r\n+ },\r\n \"gemini-2.5-pro-preview-03-25\": {\r\n \"friendly_name\": \"Gemini 2.5 Pro Preview (Mar)\",\r\n \"input\": [\r\n@@ -266,12 +300,29 @@\r\n ]\r\n }\r\n },\r\n+ \"gemini-3-flash-preview\": {\r\n+ \"friendly_name\": \"Gemini 3 Flash Preview\",\r\n+ \"input\": [\r\n+ {\r\n+ \"up_to\": \"inf\",\r\n+ \"price\": 0.5\r\n+ }\r\n+ ],\r\n+ \"output\": {\r\n+ \"default\": [\r\n+ {\r\n+ \"up_to\": \"inf\",\r\n+ \"price\": 3.0\r\n+ }\r\n+ ]\r\n+ }\r\n+ },\r\n \"gemini-2.5-flash\": {\r\n \"friendly_name\": \"Gemini 2.5 Flash\",\r\n \"input\": [\r\n {\r\n \"up_to\": \"inf\",\r\n- \"price\": 0.3\r\n+ \"price\": 0.15\r\n }\r\n ],\r\n \"output\": {\r\n@@ -295,7 +346,13 @@\r\n \"default\": [\r\n {\r\n \"up_to\": \"inf\",\r\n- \"price\": 2.5\r\n+ \"price\": 0.6\r\n+ }\r\n+ ],\r\n+ \"thinking\": [\r\n+ {\r\n+ \"up_to\": \"inf\",\r\n+ \"price\": 3.5\r\n }\r\n ]\r\n }\r\n@@ -317,8 +374,8 @@\r\n ]\r\n }\r\n },\r\n- \"gemini-2.5-flash-lite\": {\r\n- \"friendly_name\": \"Gemini 2.5 Flash Lite\",\r\n+ \"gemini-2.5-flash-lite-preview-09-25\": {\r\n+ \"friendly_name\": \"Gemini 2.5 Flash Lite Preview (Sep)\",\r\n \"input\": [\r\n {\r\n \"up_to\": \"inf\",\r\n@@ -334,8 +391,8 @@\r\n ]\r\n }\r\n },\r\n- \"gemini-2.5-flash-lite-preview-09-2025\": {\r\n- \"friendly_name\": \"Gemini 2.5 Flash Lite Preview (Sept)\",\r\n+ \"gemini-2.5-flash-lite\": {\r\n+ \"friendly_name\": \"Gemini 2.5 Flash Lite\",\r\n \"input\": [\r\n {\r\n \"up_to\": \"inf\",\r\n@@ -373,14 +430,31 @@\r\n \"input\": [\r\n {\r\n \"up_to\": \"inf\",\r\n- \"price\": 10.0\r\n+ \"price\": 2.0\r\n+ }\r\n+ ],\r\n+ \"output\": {\r\n+ \"default\": [\r\n+ {\r\n+ \"up_to\": \"inf\",\r\n+ \"price\": 8.0\r\n+ }\r\n+ ]\r\n+ }\r\n+ },\r\n+ \"openai:o3-pro\": {\r\n+ \"friendly_name\": \"OpenAI o3-pro\",\r\n+ \"input\": [\r\n+ {\r\n+ \"up_to\": \"inf\",\r\n+ \"price\": 20.0\r\n }\r\n ],\r\n \"output\": {\r\n \"default\": [\r\n {\r\n \"up_to\": \"inf\",\r\n- \"price\": 40.0\r\n+ \"price\": 80.0\r\n }\r\n ]\r\n }\r\n@@ -436,6 +510,71 @@\r\n ]\r\n }\r\n },\r\n+ \"openai:gpt-5\": {\r\n+ \"friendly_name\": \"GPT-5\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 1.25}],\r\n+ \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 10.0}]}\r\n+ },\r\n+ \"openai:gpt-5-mini\": {\r\n+ \"friendly_name\": \"GPT-5 Mini\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 0.25}],\r\n+ \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 2.0}]}\r\n+ },\r\n+ \"openai:gpt-5-nano\": {\r\n+ \"friendly_name\": \"GPT-5 Nano\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 0.05}],\r\n+ \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 0.4}]}\r\n+ },\r\n+ \"openai:gpt-5-pro\": {\r\n+ \"friendly_name\": \"GPT-5 Pro\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 15.0}],\r\n+ \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 120.0}]}\r\n+ },\r\n+ \"openai:gpt-5-chat-latest\": {\r\n+ \"friendly_name\": \"GPT-5 Chat Latest\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 1.25}],\r\n+ \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 10.0}]}\r\n+ },\r\n+ \"openai:gpt-5-codex\": {\r\n+ \"friendly_name\": \"GPT-5 Codex\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 1.25}],\r\n+ \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 10.0}]}\r\n+ },\r\n+ \"openai:gpt-5.1\": {\r\n+ \"friendly_name\": \"GPT-5.1\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 1.25}],\r\n+ \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 10.0}]}\r\n+ },\r\n+ \"openai:gpt-5.1-codex\": {\r\n+ \"friendly_name\": \"GPT-5.1 Codex\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 1.25}],\r\n+ \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 10.0}]}\r\n+ },\r\n+ \"openai:gpt-5.1-codex-max\": {\r\n+ \"friendly_name\": \"GPT-5.1 Codex Max\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 1.25}],\r\n+ \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 10.0}]}\r\n+ },\r\n+ \"openai:gpt-5.1-codex-mini\": {\r\n+ \"friendly_name\": \"GPT-5.1 Codex Mini\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 0.25}],\r\n+ \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 2.0}]}\r\n+ },\r\n+ \"openai:gpt-5-search-api\": {\r\n+ \"friendly_name\": \"GPT-5 Search API\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 1.25}],\r\n+ \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 10.0}]}\r\n+ },\r\n+ \"openai:gpt-5.2\": {\r\n+ \"friendly_name\": \"GPT-5.2\",\r\n+ \"input\": [{\"up_to\": \"inf\", \"price\": 1.75}],\r\n+ \"out…14 tokens truncated… }\r\n+}\r\n+```\r\n+\r\n+## Architecture\r\n+\r\n+### Components\r\n+\r\n+1. **parse_model_string()**: Extracts provider and model name from string\r\n+2. **create_model()**: Creates appropriate model instance or returns string for PydanticAI\r\n+3. **create_agent()**: Convenience function to create agents with proper model handling\r\n+4. **PROVIDER_CONFIGS**: Central configuration for all providers\r\n+\r\n+### Flow\r\n+\r\n+1. Model string is parsed to identify provider\r\n+2. If native provider (Google/OpenAI/Anthropic), return string for PydanticAI\r\n+3. If configured provider, create appropriate model instance\r\n+4. If unknown provider, return string and let PydanticAI handle it\r\n+5. Apply model profiles if specified in configuration\r\n+\r\n+## Benefits\r\n+\r\n+1. **Centralized Configuration**: All provider settings in one place\r\n+2. **Extensibility**: Easy to add new providers\r\n+3. **Consistency**: Same interface regardless of provider\r\n+4. **Flexibility**: Supports both native and custom providers\r\n+5. **Type Safety**: Leverages PydanticAI's type system\r\n+\r\n+## Example: Multi-Provider Comparison\r\n+\r\n+```python\r\n+from agents_mcp_usage.factory.model_factory import create_agent\r\n+\r\n+models = [\r\n+ \"gemini-2.5-pro\",\r\n+ \"openai:gpt-4\",\r\n+ \"deepseek:deepseek-chat\",\r\n+ \"ollama:llama3.2\"\r\n+]\r\n+\r\n+for model in models:\r\n+ agent = create_agent(\r\n+ model=model,\r\n+ mcp_servers=get_mcp_servers()\r\n+ )\r\n+ result = await agent.run(\"Solve this problem...\")\r\n+ print(f\"{model}: {result}\")\r\n+```\r\n+\r\n+## Environment Variables\r\n+\r\n+Most providers require API keys via environment variables:\r\n+\r\n+```bash\r\n+# Native providers\r\n+export GEMINI_API_KEY=\"...\"\r\n+export OPENAI_API_KEY=\"...\"\r\n+export ANTHROPIC_API_KEY=\"...\"\r\n+\r\n+ # Extended providers\r\n+ export DEEPSEEK_API_KEY=\"...\"\r\n+ export OPENROUTER_API_KEY=\"...\"\r\n+ export OPENROUTER_APP_URL=\"...\" # optional (app attribution)\r\n+ export OPENROUTER_APP_TITLE=\"...\" # optional (app attribution)\r\n+ export GITHUB_API_KEY=\"...\"\r\n+ export XAI_API_KEY=\"...\"\r\n+ export PERPLEXITY_API_KEY=\"...\"\r\n+\r\n+# AWS Bedrock\r\n+export AWS_REGION=\"us-east-1\"\r\n+export AWS_PROFILE=\"default\"\r\n+```\r\n+\r\n+## Migration Guide\r\n+\r\n+### Old Approach\r\n+```python\r\n+if model.startswith(\"bedrock:\"):\r\n+ from pydantic_ai.models.bedrock import BedrockConverseModel\r\n+ from pydantic_ai.providers.bedrock import BedrockProvider\r\n+ \r\n+ model_name = model.replace(\"bedrock:\", \"\")\r\n+ bedrock_model = BedrockConverseModel(\r\n+ model_name,\r\n+ provider=BedrockProvider(\r\n+ region_name=os.getenv(\"AWS_REGION\", \"us-east-1\"),\r\n+ profile_name=os.getenv(\"AWS_PROFILE\", \"default\")\r\n+ )\r\n+ )\r\n+ agent = Agent(bedrock_model, ...)\r\n+```\r\n+\r\n+### New Approach\r\n+```python\r\n+from agents_mcp_usage.factory.model_factory import create_agent\r\n+\r\n+agent = create_agent(\r\n+ model=\"bedrock:us.amazon.nova-pro-v1:0\",\r\n+ mcp_servers=get_mcp_servers()\r\n+)\r\n+```\r\n+\r\n+The model factory handles all the provider-specific logic internally, providing a cleaner and more maintainable approach.\r\ndiff --git a/agents_mcp_usage/factory/model_factory.py b/agents_mcp_usage/factory/model_factory.py\r\nnew file mode 100644\r\nindex 0000000..7b62fd2\r\n--- /dev/null\r\n+++ b/agents_mcp_usage/factory/model_factory.py\r\n@@ -0,0 +1,431 @@\r\n+\"\"\"\r\n+Model Factory for PydanticAI\r\n+\r\n+This module provides a centralized way to create models across different providers,\r\n+leveraging PydanticAI's built-in provider support and handling special cases.\r\n+\"\"\"\r\n+\r\n+import os\r\n+import re\r\n+from typing import Any, Dict, Optional, Union\r\n+from pydantic_ai import Agent, InlineDefsJsonSchemaTransformer\r\n+from pydantic_ai.models import Model\r\n+from pydantic_ai.models.openai import OpenAIResponsesModel\r\n+from pydantic_ai.profiles.openai import OpenAIModelProfile\r\n+from pydantic_ai.providers.openai import OpenAIProvider\r\n+\r\n+\r\n+# Providers that PydanticAI handles natively with provider:model syntax.\r\n+#\r\n+# NOTE:\r\n+# - `openrouter:*` is intentionally **not** treated as native in this repo.\r\n+# We construct an explicit OpenRouter provider + OpenAI-compatible model so we\r\n+# can control configuration and avoid relying on implicit provider detection.\r\n+NATIVE_PROVIDERS = {\r\n+ \"deepseek\", \"github\", \"grok\", \"azure\",\r\n+ \"fireworks\", \"together\", \"heroku\"\r\n+}\r\n+\r\n+# Special cases that need custom handling\r\n+PROVIDER_CONFIGS = {\r\n+ \"bedrock\": {\r\n+ # Bedrock uses a different model class\r\n+ \"handler\": \"bedrock_handler\",\r\n+ \"env_vars\": {\r\n+ \"region\": \"AWS_REGION\",\r\n+ \"profile\": \"AWS_PROFILE\"\r\n+ }\r\n+ },\r\n+ \"ollama\": {\r\n+ # Ollama is OpenAI-compatible but needs custom base URL\r\n+ \"handler\": \"openai_compatible_handler\",\r\n+ \"base_url\": \"http://localhost:11434/v1\",\r\n+ \"api_key\": \"not-needed\",\r\n+ \"profile\": {\r\n+ \"json_schema_transformer\": InlineDefsJsonSchemaTransformer,\r\n+ \"openai_supports_strict_tool_definition\": False\r\n+ }\r\n+ },\r\n+ \"perplexity\": {\r\n+ # Perplexity is OpenAI-compatible with custom base URL\r\n+ \"handler\": \"openai_compatible_handler\",\r\n+ \"base_url\": \"https://api.perplexity.ai\",\r\n+ \"env_var\": \"PERPLEXITY_API_KEY\"\r\n+ },\r\n+ \"openrouter\": {\r\n+ # OpenRouter has first-class support in PydanticAI, but we construct the\r\n+ # provider/model explicitly so configuration is controlled by this repo.\r\n+ \"handler\": \"openrouter_handler\",\r\n+ \"env_var\": \"OPENROUTER_API_KEY\",\r\n+ \"env_vars\": {\r\n+ \"app_url\": \"OPENROUTER_APP_URL\",\r\n+ \"app_title\": \"OPENROUTER_APP_TITLE\",\r\n+ },\r\n+ }\r\n+}\r\n+\r\n+\r\n+def ensure_openai_responses_model(model_string: str) -> str:\r\n+ \"\"\"Ensure OpenAI models use the Responses API.\r\n+\r\n+ This helper normalises any OpenAI model identifier to the\r\n+ `openai-responses:` provider namespace so that PydanticAI\r\n+ will use the OpenAI Responses API rather than Chat Completions.\r\n+\r\n+ Examples:\r\n+ \"gpt-5\" -> \"openai-responses:gpt-5\"\r\n+ \"openai:gpt-5-mini\" -> \"openai-responses:gpt-5-mini\"\r\n+ \"openai-responses:gpt-5\" -> \"openai-responses:gpt-5\" (unchanged)\r\n+ \"\"\"\r\n+ # Avoid double-prefixing if already using Responses API directly\r\n+ if model_string.startswith(\"openai-responses:\"):\r\n+ return model_string\r\n+\r\n+ provider, model_name = parse_model_string(model_string)\r\n+ if provider == \"openai\":\r\n+ return f\"openai-responses:{model_name}\"\r\n+\r\n+ return model_string\r\n+\r\n+\r\n+# Accept optional reasoning hints for models that support it\r\n+REASONING_SUFFIX_RE = re.compile(r\"\\s*\\((low|medium|high|none|minimal|xhigh)\\)\\s*$\", re.IGNORECASE)\r\n+\r\n+\r\n+def extract_reasoning_effort(model_string: str) -> tuple[str, Optional[str]]:\r\n+ \"\"\"Strip trailing reasoning hint like \"(medium)\" or \"(none)\" from model string.\r\n+\r\n+ Returns the base model string and the effort level (lowercased) if present.\r\n+ \"\"\"\r\n+\r\n+ match = REASONING_SUFFIX_RE.search(model_string)\r\n+ if not match:\r\n+ return model_string.strip(), None\r\n+\r\n+ effort = match.group(1).lower()\r\n+ base = REASONING_SUFFIX_RE.sub(\"\", model_string).strip()\r\n+ return base, effort\r\n+\r\n+\r\n+def parse_model_string(model_string: str) -> tuple[Optional[str], str]:\r\n+ \"\"\"\r\n+ Parse a model string to extract provider and model name.\r\n+ \r\n+ Args:\r\n+ model_string: Model identifier (e.g., \"openai:gpt-4\", \"deepseek:deepseek-chat\", \"gemini-2.5-pro\")\r\n+ \r\n+ Returns:\r\n+ Tuple of (provider, model_name)\r\n+ \"\"\"\r\n+ if \":\" in model_string:\r\n+ provider, model_name = model_string.split(\":\", 1)\r\n+ return provider, model_name\r\n+ \r\n+ # Check for known prefixes without colon\r\n+ if model_string.startswith(\"gemini-\"):\r\n+ return \"google\", model_string\r\n+ elif model_string.startswith(\"gpt-\") or model_string.startswith(\"o1-\") or model_string.startswith(\"o3-\") or model_string.startswith(\"o4-\"):\r\n+ return \"openai\", model_string\r\n+ elif model_string.startswith(\"claude-\"):\r\n+ return \"anthropic\", model_string\r\n+ \r\n+ # Default to no specific provider\r\n+ return None, model_string\r\n+\r\n+\r\n+def handle_bedrock_model(\r\n+ model_name: str,\r\n+ provider_kwargs: Optional[Dict[str, Any]] = None\r\n+) -> Model:\r\n+ \"\"\"Handle AWS Bedrock models which require special treatment.\"\"\"\r\n+ from pydantic_ai.models.bedrock import BedrockConverseModel\r\n+ from pydantic_ai.providers.bedrock import BedrockProvider\r\n+ \r\n+ config = PROVIDER_CONFIGS[\"bedrock\"]\r\n+ provider_kwargs = provider_kwargs or {}\r\n+ \r\n+ region = provider_kwargs.get(\"region_name\") or os.getenv(config[\"env_vars\"][\"region\"], \"us-east-1\")\r\n+ profile = provider_kwargs.get(\"profile_name\") or os.getenv(config[\"env_vars\"][\"profile\"], \"default\")\r\n+ \r\n+ provider_instance = BedrockProvider(\r\n+ region_name=region,\r\n+ profile_name=profile\r\n+ )\r\n+ \r\n+ return BedrockConverseModel(\r\n+ model_name,\r\n+ provider=provider_instance\r\n+ )\r\n+\r\n+\r\n+def handle_openai_compatible(\r\n+ provider: str,\r\n+ model_name: str,\r\n+ provider_kwargs: Optional[Dict[str, Any]] = None\r\n+) -> Model:\r\n+ \"\"\"Handle OpenAI-compatible providers that need custom configuration.\"\"\"\r\n+ config = PROVIDER_CONFIGS[provider]\r\n+ provider_kwargs = provider_kwargs or {}\r\n+ \r\n+ # Build OpenAI provider kwargs\r\n+ openai_kwargs = {}\r\n+ \r\n+ # Set base URL\r\n+ if \"base_url\" in config:\r\n+ openai_kwargs[\"base_url\"] = provider_kwargs.get(\"base_url\", config[\"base_url\"])\r\n+ \r\n+ # Handle API key\r\n+ if \"api_key\" in config:\r\n+ openai_kwargs[\"api_key\"] = config[\"api_key\"]\r\n+ elif \"env_var\" in config:\r\n+ api_key = provider_kwargs.get(\"api_key\") or os.getenv(config[\"env_var\"])\r\n+ if api_key:\r\n+ openai_kwargs[\"api_key\"] = api_key\r\n+ \r\n+ # Create OpenAI provider\r\n+ provider_instance = OpenAIProvider(**openai_kwargs)\r\n+ \r\n+ # Create model with profile if specified\r\n+ if config.get(\"profile\"):\r\n+ profile = OpenAIModelProfile(**config[\"profile\"])\r\n+ return OpenAIResponsesModel(\r\n+ model_name,\r\n+ provider=provider_instance,\r\n+ profile=profile,\r\n+ )\r\n+ else:\r\n+ return OpenAIResponsesModel(\r\n+ model_name,\r\n+ provider=provider_instance,\r\n+ )\r\n+\r\n+\r\n+def handle_openrouter_model(\r\n+ model_name: str,\r\n+ provider_kwargs: Optional[Dict[str, Any]] = None,\r\n+) -> Model:\r\n+ \"\"\"Handle OpenRouter models via an explicit provider/model.\r\n+\r\n+ PydanticAI can accept `openrouter:` strings directly, but we build\r\n+ the provider/model explicitly so this repository owns configuration and can\r\n+ support optional app attribution.\r\n+ \"\"\"\r\n+\r\n+ # Import lazily so users without the optional dependency can still import\r\n+ # the rest of the repo.\r\n+ #\r\n+ # NOTE: In pydantic-ai-slim==1.17.x, OpenRouter uses the OpenAI-compatible\r\n+ # models (Chat Completions) with an `OpenRouterProvider`, rather than a\r\n+ # dedicated `OpenRouterModel` class.\r\n+ import json\r\n+\r\n+ from openai import AsyncOpenAI\r\n+ from openai.types import chat\r\n+ from pydantic import ValidationError\r\n+ from pydantic_ai.models.openai import OpenAIChatModel\r\n+ from pydantic_ai.providers.openrouter import OpenRouterProvider\r\n+\r\n+ class OpenRouterPatchedChatModel(OpenAIChatModel):\r\n+ \"\"\"OpenAIChatModel with OpenRouter response sanitation.\r\n+\r\n+ Some OpenRouter-backed models (notably Anthropic) occasionally return\r\n+ invalid tool-call payloads such as `function.arguments = null`. PydanticAI\r\n+ validates the OpenAI response strictly, which raises\r\n+ `UnexpectedModelBehavior` before tool execution can happen.\r\n+\r\n+ We patch the raw ChatCompletion dict prior to Pydantic validation.\r\n+ \"\"\"\r\n+\r\n+ def _process_response(self, response: chat.ChatCompletion | str): # type: ignore[override]\r\n+ if isinstance(response, chat.ChatCompletion):\r\n+ data = response.model_dump()\r\n+\r\n+ # Fix invalid tool_calls payloads (e.g. `arguments: null`)\r\n+ try:\r\n+ choices = data.get(\"choices\")\r\n+ if isinstance(choices, list):\r\n+ for choice in choices:\r\n+ if not isinstance(choice, dict):\r\n+ continue\r\n+ message = choice.get(\"message\")\r\n+ if not isinstance(message, dict):\r\n+ continue\r\n+ tool_calls = message.get(\"tool_calls\")\r\n+ if not isinstance(tool_calls, list):\r\n+ continue\r\n+ for call in tool_calls:\r\n+ if not isinstance(call, dict):\r\n+ continue\r\n+ if call.get(\"type\") != \"function\":\r\n+ continue\r\n+ fn = call.get(\"function\")\r\n+ if not isinstance(fn, dict):\r\n+ fn = {}\r\n+ call[\"function\"] = fn\r\n+ args = fn.get(\"arguments\")\r\n+ if args is None:\r\n+ fn[\"arguments\"] = \"{}\"\r\n+ elif not isinstance(args, str):\r\n+ fn[\"arguments\"] = json.dumps(args)\r\n+ except Exception:\r\n+ # Best-effort only; fall through to default handling.\r\n+ pass\r\n+\r\n+ try:\r\n+ response = chat.ChatCompletion.model_validate(data)\r\n+ except ValidationError:\r\n+ # If sanitation didn't help, let the base model raise a\r\n+ # helpful UnexpectedModelBehavior.\r\n+ pass\r\n+\r\n+ return super()._process_response(response)\r\n+\r\n+ config = PROVIDER_CONFIGS[\"openrouter\"]\r\n+ provider_kwargs = provider_kwargs or {}\r\n+\r\n+ api_key = provider_kwargs.get(\"api_key\") or os.getenv(config[\"env_var\"])\r\n+ if not api_key:\r\n+ raise ValueError(\r\n+ \"OpenRouter API key not configured. \"\r\n+ \"Set OPENROUTER_API_KEY or pass provider_kwargs={'api_key': '...'}\"\r\n+ )\r\n+\r\n+ app_url = provider_kwargs.get(\"app_url\") or os.getenv(config[\"env_vars\"][\"app_url\"])\r\n+ app_title = provider_kwargs.get(\"app_title\") or os.getenv(config[\"env_vars\"][\"app_title\"])\r\n+\r\n+ # OpenRouter app attribution uses HTTP headers:\r\n+ # - HTTP-Referer: your app URL\r\n+ # - X-Title: your app name\r\n+ # See: https://openrouter.ai/docs/app-attribution\r\n+ default_headers: dict[str, str] = {}\r\n+ if app_url:\r\n+ default_headers[\"HTTP-Referer\"] = app_url\r\n+ if app_title:\r\n+ default_headers[\"X-Title\"] = app_title\r\n+\r\n+ base_url = provider_kwargs.get(\"base_url\") or \"https://openrouter.ai/api/v1\"\r\n+ openai_client = AsyncOpenAI(\r\n+ base_url=base_url,\r\n+ api_key=api_key,\r\n+ default_headers=default_headers or None,\r\n+ )\r\n+ provider_instance = OpenRouterProvider(openai_client=openai_client)\r\n+\r\n+ return OpenRouterPatchedChatModel(model_name, provider=provider_instance)\r\n+\r\n+\r\n+def create_model(\r\n+ model_string: str,\r\n+ model_settings: Optional[Dict[str, Any]] = None,\r\n+ provider_kwargs: Optional[Dict[str, Any]] = None\r\n+) -> Union[Model, str]:\r\n+ \"\"\"\r\n+ Create a model instance from a model string.\r\n+ \r\n+ This function:\r\n+ 1. Returns the string directly for providers PydanticAI handles natively\r\n+ 2. Handles special cases like Bedrock and OpenAI-compatible providers\r\n+ 3. Falls back to returning the string for unknown providers\r\n+ \r\n+ Args:\r\n+ model_string: Model identifier\r\n+ model_settings: Optional model-specific settings\r\n+ provider_kwargs: Optional provider-specific kwargs (e.g., azure_endpoint)\r\n+ \r\n+ Returns:\r\n+ Either a Model instance or the original string for PydanticAI to handle\r\n+ \"\"\"\r\n+ # First, normalise any OpenAI models to use the Responses API\r\n+ model_string = ensure_openai_responses_model(model_string)\r\n+\r\n+ provider, model_name = parse_model_string(model_string)\r\n+ \r\n+ # No provider prefix - let PydanticAI handle it\r\n+ if provider is None:\r\n+ return model_string\r\n+ \r\n+ # Standard providers that PydanticAI auto-detects\r\n+ # Note: OpenAI models have been converted to \"openai-responses:*\" above\r\n+ if provider in [\"google\", \"anthropic\"]:\r\n+ return model_string\r\n+ \r\n+ # Providers with native PydanticAI support\r\n+ if provider in NATIVE_PROVIDERS:\r\n+ # Special validation for Azure\r\n+ if provider == \"azure\" and provider_kwargs:\r\n+ required = [\"azure_endpoint\", \"api_version\"]\r\n+ missing = [k for k in required if k not in provider_kwargs]\r\n+ if missing:\r\n+ raise ValueError(\r\n+ f\"Azure provider requires {missing} in provider_kwargs. \"\r\n+ f\"Alternatively, let PydanticAI handle it by using the string directly.\"\r\n+ )\r\n+ # Return the string - PydanticAI will handle it\r\n+ return model_string\r\n+ \r\n+ # Handle special cases\r\n+ if provider in PROVIDER_CONFIGS:\r\n+ config = PROVIDER_CONFIGS[provider]\r\n+ handler = config.get(\"handler\")\r\n+ \r\n+ if handler == \"bedrock_handler\":\r\n+ return handle_bedrock_model(model_name, provider_kwargs)\r\n+ elif handler == \"openai_compatible_handler\":\r\n+ return handle_openai_compatible(provider, model_name, provider_kwargs)\r\n+ elif handler == \"openrouter_handler\":\r\n+ return handle_openrouter_model(model_name, provider_kwargs)\r\n+ \r\n+ # Unknown provider - return string and let PydanticAI try\r\n+ return model_string\r\n+\r\n+\r\n+def create_agent(\r\n+ model: Union[str, Model],\r\n+ mcp_servers=None,\r\n+ model_settings: Optional[Dict[str, Any]] = None,\r\n+ provider_kwargs: Optional[Dict[str, Any]] = None,\r\n+ **agent_kwargs\r\n+) -> Agent:\r\n+ \"\"\"\r\n+ Create an Agent with proper model handling.\r\n+ \r\n+ This is a convenience function that:\r\n+ 1. Converts model strings to appropriate Model instances when needed\r\n+ 2. Passes through Model instances unchanged\r\n+ 3. Leverages PydanticAI's native provider support\r\n+ \r\n+ Args:\r\n+ model: Model string or Model instance\r\n+ mcp_servers: Optional MCP servers\r\n+ model_settings: Optional model settings\r\n+ provider_kwargs: Optional provider-specific kwargs\r\n+ **agent_kwargs: Additional Agent kwargs\r\n+ \r\n+ Returns:\r\n+ Configured Agent instance\r\n+ \"\"\"\r\n+ if isinstance(model, str):\r\n+ base_model, reasoning_effort = extract_reasoning_effort(model)\r\n+\r\n+ # Ensure we have a dict to attach model-level settings\r\n+ if model_settings is None:\r\n+ model_settings = {}\r\n+\r\n+ # Only apply reasoning hint to OpenAI Responses-capable models\r\n+ provider, _ = parse_model_string(base_model)\r\n+ if (\r\n+ reasoning_effort\r\n+ and isinstance(model_settings, dict)\r\n+ and provider in {\"openai\", \"openai-responses\"}\r\n+ and \"openai_reasoning_effort\" not in model_settings\r\n+ ):\r\n+ model_settings[\"openai_reasoning_effort\"] = reasoning_effort\r\n+\r\n+ model = create_model(base_model, model_settings, provider_kwargs)\r\n+\r\n+ return Agent(\r\n+ model,\r\n+ mcp_servers=mcp_servers,\r\n+ model_settings=model_settings if isinstance(model, str) else None,\r\n+ **agent_kwargs\r\n+ 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create a PR description for me based on the changes in this branch.\nJan/December 2025 updates\n\n",{},"created",1768425783.237019,"Completing the task","event_type","info",{"_90":677,"_94":91,"_673":5546},{"_90":2567,"_2568":2569},"partial_repo_snapshot","files",[2570,2571,2572,2573,2574,2575,2576,2577,2578,2579,2580,2581],{"_216":1468,"_2582":-5,"_2583":5262,"_2585":-5},{"_216":844,"_2582":-5,"_2583":5106,"_2585":-5},{"_216":1542,"_2582":-5,"_2583":4972,"_2585":-5},{"_216":750,"_2582":-5,"_2583":4792,"_2585":-5},{"_216":882,"_2582":-5,"_2583":4006,"_2585":-5},{"_216":1394,"_2582":-5,"_2583":3657,"_2585":-5},{"_216":1110,"_2582":-5,"_2583":3328,"_2585":-5},{"_216":806,"_2582":-5,"_2583":3247,"_2585":-5},{"_216":1072,"_2582":-5,"_2583":2924,"_2585":-5},{"_216":1258,"_2582":-5,"_2583":2704,"_2585":-5},{"_216":1357,"_2582":-5,"_2583":2694,"_2585":-5},{"_216":1034,"_2582":-5,"_2583":2584,"_2585":-5},"contents","line_range_contents",[2586],"missing_reason",{"_2587":990,"_2588":2589,"_673":2590,"_2591":57},"line_range_start","line_range_end",132,[2592,2593,2594,2595,2596,2597,2598,2599,2600,315,2601,315,2602,315,315,2603,2604,315,2605,2606,2607,2608,2609,2610,2608,315,315,2611,2612,2613,315,2614,2615,2616,315,315,2617,2618,315,2619,2620,2621,2622,2623,2624,315,2625,2626,2627,2628,2629,2630,315,2631,2632,2633,315,2634,2626,2635,2636,2637,2638,315,2639,2640,2641,2642,2643,2644,2645,2646,2647,2648,2649,2650,2651,2652,2653,315,2654,2655,2656,315,2657,2658,2659,2660,2657,2661,2662,2663,2659,2664,2665,2666,2667,2668,2653,2669,2670,2671,2672,2673,2674,2675,2676,315,315,2677,2678,2679,315,2619,2680,2621,2681,2682,2683,2684,2685,2686,2687,2688,2689,2690,315,315,2691,2692,315,2693],"contains_end_of_file","import os","import tempfile","import subprocess","import base64","import json","from typing import Optional","from pydantic import BaseModel, Field","from dotenv import load_dotenv","from mcp.server.fastmcp import FastMCP","load_dotenv()","mcp = FastMCP(\"mermaid-validator\")","class MermaidValidationResult(BaseModel):"," \"\"\"Result of mermaid diagram validation.\"\"\""," is_valid: bool = Field(description=\"Whether the mermaid diagram is valid\")"," error_message: Optional[str] = Field("," None, description=\"Error message if the diagram is invalid\""," )"," diagram_image: Optional[str] = Field("," None, description=\"Base64-encoded PNG image of the rendered diagram if valid\"","@mcp.tool()","async def validate_mermaid_diagram(diagram_text: str) -> MermaidValidationResult:"," \"\"\"Validates a mermaid diagram and renders it as a PNG image if valid."," This function uses the mermaid-cli to validate and render the provided"," mermaid diagram text.Requires mermaid-cli to be installed globally via npm"," `npm install -g @mermaid-js/mermaid-cli`"," Args:"," diagram_text: The mermaid diagram text to validate."," Returns:"," A MermaidValidationResult object containing the validation results."," \"\"\""," temp_file_path = None"," output_file_name = None"," puppeteer_config_path = None"," try:"," with tempfile.NamedTemporaryFile("," suffix=\".mmd\", mode=\"w\", delete=False"," ) as temp_file:"," temp_file.write(diagram_text)"," temp_file_path = temp_file.name"," output_file = tempfile.NamedTemporaryFile(suffix=\".png\", delete=False)"," output_file.close()"," output_file_name = output_file.name"," puppeteer_config = {\"args\": [\"--no-sandbox\", \"--disable-setuid-sandbox\"]}"," suffix=\".json\", mode=\"w\", delete=False"," ) as config_file:"," json.dump(puppeteer_config, config_file)"," puppeteer_config_path = config_file.name"," result = subprocess.run("," ["," \"npx\","," \"-y\","," \"@mermaid-js/mermaid-cli@11.4.2\","," \"-i\","," temp_file_path,"," \"-o\","," output_file_name,"," \"--puppeteerConfigFile\","," puppeteer_config_path,"," ],"," capture_output=True,"," text=True,"," )"," if result.returncode == 0:"," with open(output_file_name, \"rb\") as f:"," diagram_image = base64.b64encode(f.read()).decode(\"utf-8\")"," return MermaidValidationResult("," is_valid=True, error_message=None, diagram_image=diagram_image"," )"," else:"," is_valid=False,"," error_message=f\"Mermaid diagram is invalid: {result.stderr}\","," diagram_image=None,"," except Exception as e:"," return MermaidValidationResult("," is_valid=False,"," error_message=f\"Error validating mermaid diagram: {str(e)}\","," diagram_image=None,"," finally:"," for file_path in [temp_file_path, output_file_name, puppeteer_config_path]:"," if file_path and os.path.exists(file_path):"," try:"," os.unlink(file_path)"," except OSError:"," # Ignore file deletion errors during cleanup"," pass","@mcp.resource(\"example://mermaid-diagram\")","def get_example_mermaid_diagram() -> dict:"," \"\"\"Provides an example mermaid diagram for the client."," A dictionary containing an example mermaid diagram."," return {"," \"diagram\": \"\"\""," graph TD"," A[Start] --> B{Is it valid?}"," B -->|Yes| C[Output valid result]"," B -->|No| D[Output error message]"," C --> E[End]"," D --> E"," \"\"\""," }","if __name__ == \"__main__\":"," transport = os.getenv(\"MCP_TRANSPORT\", \"stdio\")"," 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\"openai:gpt-5-pro\": {"," \"friendly_name\": \"GPT-5 Pro\","," \"input\": [{\"up_to\": \"inf\", \"price\": 15.0}],"," \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 120.0}]}"," \"openai:gpt-5-chat-latest\": {"," \"friendly_name\": \"GPT-5 Chat Latest\","," \"openai:gpt-5-codex\": {"," \"friendly_name\": \"GPT-5 Codex\","," \"openai:gpt-5.1\": {"," \"friendly_name\": \"GPT-5.1\","," \"openai:gpt-5.1-codex\": {"," \"friendly_name\": \"GPT-5.1 Codex\","," \"openai:gpt-5.1-codex-max\": {"," \"friendly_name\": \"GPT-5.1 Codex Max\","," \"openai:gpt-5.1-codex-mini\": {"," \"friendly_name\": \"GPT-5.1 Codex Mini\","," \"openai:gpt-5-search-api\": {"," \"friendly_name\": \"GPT-5 Search API\","," \"openai:gpt-5.2\": {"," \"friendly_name\": \"GPT-5.2\","," \"input\": [{\"up_to\": \"inf\", \"price\": 1.75}],"," \"output\": {\"default\": [{\"up_to\": \"inf\", \"price\": 14.0}]}"," \"openai:gpt-5.2-codex\": {"," \"friendly_name\": \"GPT-5.2 Codex\","," 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\"bedrock:us.amazon.nova-premier-v1:0\": {"," \"friendly_name\": \"Amazon Nova Premier\","," \"price\": 2.50"," \"price\": 12.50"," \"openrouter:anthropic/claude-sonnet-4.5\": {"," \"friendly_name\": \"Claude Sonnet 4.5\","," \"price\": 6.0"," \"price\": 22.5"," \"openrouter:anthropic/claude-opus-4.5\": {"," \"friendly_name\": \"Claude Opus 4.5\","," \"price\": 5.0"," \"price\": 25.0"," \"deepseek:deepseek-chat\": {"," \"friendly_name\": \"DeepSeek Chat (V3)\","," \"price\": 0.27"," \"price\": 1.10"," \"deepseek:deepseek-reasoner\": {"," \"friendly_name\": \"DeepSeek Reasoner (R1)\","," \"price\": 0.55"," \"price\": 2.19"," \"github:xai/grok-3-mini\": {"," \"friendly_name\": \"Grok 3 Mini (GitHub)\","," \"price\": 0.25"," \"price\": 1.27"," \"grok:grok-2-1212\": {"," \"friendly_name\": \"Grok 2\","," \"grok:grok-4\": {"," \"friendly_name\": \"Grok 4\","," \"perplexity:sonar-pro\": {"," \"friendly_name\": \"Perplexity Sonar Pro\","," \"perplexity:sonar\": {"," \"friendly_name\": \"Perplexity Sonar\","," \"price\": 1.0"," \"ollama:llama3.2\": {"," \"friendly_name\": \"Llama 3.2 (Local)\","," \"price\": 0.0"," \"price\": 0.0"," \"ollama:qwen2.5-coder:7b\": {"," \"friendly_name\": \"Qwen 2.5 Coder 7B (Local)\","," 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python3","import argparse","import re","import sys","from datetime import datetime","from pathlib import Path","from typing import Any, Dict, List","import pandas as pd","# Add parent directory to path to import modules","sys.path.append(str(Path(__file__).parent.parent))","from agents_mcp_usage.evaluations.mermaid_evals.dashboard_config import DEFAULT_CONFIG","from agents_mcp_usage.evaluations.mermaid_evals.schemas import DashboardConfig","from agents_mcp_usage.evaluations.mermaid_evals.evals_pydantic_mcp import ("," REQUEST_USAGE_COLUMN,",")","from agents_mcp_usage.utils import get_project_root","REASONING_SUFFIX_RE = re.compile(r\"\\s*\\((low|medium|high)\\)\\s*$\", re.IGNORECASE)","def normalize_model_name(model_name: Any) -> Any:"," \"\"\"Normalize model names for cost lookup."," - Strip trailing reasoning hints like \"(medium)\"."," - Map OpenAI Responses provider prefix to the standard OpenAI key used in costs.json."," - Add an \"openai:\" prefix for bare GPT model names."," if not isinstance(model_name, str):"," return model_name"," name = model_name.strip()"," name = REASONING_SUFFIX_RE.sub(\"\", name).strip()"," if name.startswith(\"openai-responses:\"):"," name = \"openai:\" + name.split(\":\", 1)[1]"," elif name.startswith(\"gpt-\") and \":\" not in name:"," name = f\"openai:{name}\""," return name","def load_model_costs(file_path: Path) -> Dict[str, Any]:"," \"\"\"Load model costs from JSON file.\"\"\""," with open(file_path, 'r') as f:"," data = json.load(f)"," "," # Convert \"inf\" strings to actual infinity"," def convert_inf_strings(obj: Any) -> Any:"," if isinstance(obj, dict):"," return {k: convert_inf_strings(v) for k, v in obj.items()}"," elif isinstance(obj, list):"," return [convert_inf_strings(v) for v in obj]"," elif obj == \"inf\":"," return float(\"inf\")"," return obj"," # Extract model_costs from the loaded data"," model_costs = data.get('model_costs', {})"," return convert_inf_strings(model_costs)","def get_price_for_tokens(token_count: int, price_tiers: List[Dict]) -> float:"," \"\"\"Find the correct price for a given number of tokens from a list of tiers.\"\"\""," for tier in price_tiers:"," if token_count <= tier[\"up_to\"]:"," return tier[\"price\"]"," return price_tiers[-1][\"price\"] # Fallback to the highest tier price","def calculate_costs(df: pd.DataFrame, cost_config: Dict, config: DashboardConfig) -> pd.DataFrame:"," \"\"\"Calculate input, output, and total costs for each run based on tiered pricing."," Sets cost to 0 if Score_UsageLimitNotExceeded == 0."," df_with_costs = df.copy()"," # Get cost calculation config from dashboard config"," cost_calc_config = config.cost_calculation"," input_token_cols = cost_calc_config.input_token_cols"," output_token_cols = cost_calc_config.output_token_cols"," # Initialize cost columns"," df_with_costs[\"input_cost\"] = 0.0"," df_with_costs[\"output_cost\"] = 0.0"," df_with_costs[\"total_cost\"] = 0.0"," for idx, row in df_with_costs.iterrows():"," # Check if usage limit was exceeded - if so, cost is 0"," if row.get(\"Score_UsageLimitNotExceeded\", 1) == 0:"," continue"," "," model = row.get(\"Model\")"," model_key = normalize_model_name(model)"," model_costs = cost_config.get(model_key)"," if not model_costs:"," try:"," # Calculate token counts"," input_tokens = sum(row.get(col, 0) or 0 for col in input_token_cols)"," output_tokens = sum(row.get(col, 0) or 0 for col in output_token_cols)"," thinking_tokens = row.get(\"thinking_tokens\", 0) or 0"," non_thinking_output_tokens = output_tokens - thinking_tokens"," total_tokens = input_tokens + output_tokens"," # Calculate input cost"," input_price_tiers = model_costs.get(\"input\", [])"," if input_price_tiers:"," input_price = get_price_for_tokens(total_tokens, input_price_tiers)"," input_cost = (input_tokens / 1_000_000) * input_price"," else:"," input_cost = 0.0"," "," # Calculate output cost"," output_cost = 0.0"," output_pricing = model_costs.get(\"output\", {})"," if \"thinking\" in output_pricing and thinking_tokens > 0:"," thinking_price_tiers = output_pricing[\"thinking\"]"," thinking_price = get_price_for_tokens(total_tokens, thinking_price_tiers)"," output_cost += (thinking_tokens / 1_000_000) * thinking_price"," if \"non_thinking\" in output_pricing and non_thinking_output_tokens > 0:"," non_thinking_price_tiers = output_pricing[\"non_thinking\"]"," non_thinking_price = get_price_for_tokens(total_tokens, non_thinking_price_tiers)"," output_cost += (non_thinking_output_tokens / 1_000_000) * non_thinking_price"," elif \"default\" in output_pricing:"," default_price_tiers = output_pricing[\"default\"]"," default_price = get_price_for_tokens(total_tokens, default_price_tiers)"," output_cost += (output_tokens / 1_000_000) * default_price"," df_with_costs.at[idx, \"input_cost\"] = input_cost"," df_with_costs.at[idx, \"output_cost\"] = output_cost"," df_with_costs.at[idx, \"total_cost\"] = input_cost + output_cost"," except (TypeError, KeyError, IndexError) as e:"," print(f\"Cost calculation error for model {model} at row {idx}: {e}\")"," return df_with_costs","def parse_metric_details(metric_details_str):"," \"\"\"Safely parse JSON string from Metric_details column.\"\"\""," if pd.isna(metric_details_str) or not metric_details_str:"," return {}"," return json.loads(metric_details_str.replace(\"'\", '\"'))"," except (json.JSONDecodeError, TypeError):","def calculate_failure_analysis_data(df):"," \"\"\"Calculate failure counts by model and failure type.\"\"\""," failure_series = ["," {\"name\": \"Invalid Diagram\", \"column\": \"Score_MermaidDiagramValid\", \"condition\": \"== 0\"},"," {\"name\": \"MCP Tool Failure\", \"column\": \"Score_UsedBothMCPTools\", \"condition\": \"< 1\"},"," {\"name\": \"Usage Limit Exceeded\", \"column\": \"Score_UsageLimitNotExceeded\", \"condition\": \"== 0\"},"," ]"," models = sorted(df[\"Model\"].unique())"," failure_data = []"," for model in models:"," model_data = df[df[\"Model\"] == model]"," failure_counts = {\"Model\": model}"," "," for series in failure_series:"," condition_str = f\"`{series['column']}` {series['condition']}\""," count = model_data.eval(condition_str).sum()"," failure_counts[series[\"name\"]] = int(count)"," failure_data.append(failure_counts)"," return failure_data","def process_csv_for_static_site(csv_path):"," \"\"\"Process CSV file and return data structure for static site.\"\"\""," # Load configuration"," config = DashboardConfig(**DEFAULT_CONFIG)"," # Load cost configuration"," project_root = get_project_root()"," costs_json_path = project_root / \"agents_mcp_usage\" / \"evaluations\" / \"mermaid_evals\" / \"costs.json\""," cost_config = load_model_costs(costs_json_path) if costs_json_path.exists() else {}"," # Read CSV"," df = pd.read_csv(csv_path)"," if REQUEST_USAGE_COLUMN not in df.columns:"," df[REQUEST_USAGE_COLUMN] = \"na\""," df[REQUEST_USAGE_COLUMN] = df[REQUEST_USAGE_COLUMN].fillna(\"na\")"," # Normalize token columns so missing usage is treated as 0 and types are numeric."," token_cols = input_token_cols + output_token_cols"," for col in token_cols:"," if col not in df.columns:"," df[col] = 0"," df[col] = pd.to_numeric(df[col], errors=\"coerce\").fillna(0)"," # Extract grouping column (test case types)"," df['test_group'] = df['Case'].apply(lambda x: x.split('_')[-1] if '_' in x else 'other')"," # Parse metric details to extract token information"," if \"Metric_details\" in df.columns:"," metric_details = df[\"Metric_details\"].apply(parse_metric_details)"," df[\"thinking_tokens\"] = metric_details.apply(lambda x: x.get(\"thoughts_tokens\", 0))"," df[\"text_tokens\"] = metric_details.apply(lambda x: x.get(\"text_prompt_tokens\", 0))"," else:"," df[\"thinking_tokens\"] = 0"," df[\"text_tokens\"] = 0"," df[\"thinking_tokens\"] = pd.to_numeric(df[\"thinking_tokens\"], errors=\"coerce\").fillna(0)"," df[\"text_tokens\"] = pd.to_numeric(df[\"text_tokens\"], errors=\"coerce\").fillna(0)"," # Calculate total tokens in the same way as the local Streamlit dashboard:"," # sum of input + output token columns (including thinking tokens)."," df[\"total_tokens\"] = 0"," df[\"total_tokens\"] += df[col]"," # Calculate success rate (primary metric)"," df[\"Success_Rate\"] = df[\"Score_MermaidDiagramValid\"] * 100"," # Extract provider from model name"," def extract_provider(model_name: str) -> str:"," name = str(model_name)"," if name.startswith(\"openrouter:\"):"," openrouter_id = name.split(\":\", 1)[1]"," provider_id = ("," openrouter_id.split(\"/\", 1)[0] if \"/\" in openrouter_id else openrouter_id"," provider_key = provider_id.strip().lower()"," provider_map = {"," \"amazon\": \"Amazon\","," \"anthropic\": \"Anthropic\","," \"cohere\": \"Cohere\","," \"deepseek\": \"DeepSeek\","," \"google\": \"Google\","," \"meta-llama\": \"Meta\","," \"mistralai\": \"Mistral\","," \"moonshotai\": \"MoonshotAI\","," \"openai\": \"OpenAI\","," \"qwen\": \"Qwen\","," \"x-ai\": \"xAI\","," }"," if provider_key in provider_map:"," return provider_map[provider_key]"," fallback = provider_id.replace(\"-\", \" \").replace(\"_\", \" \").strip()"," return fallback.title() if fallback else \"OpenRouter\""," if name.startswith(\"gemini-\"):"," return \"Google\""," if \"nova\" in name.lower():"," return \"Amazon\""," if \"claude\" in name.lower():"," return \"Anthropic\""," if \"gpt\" in name.lower():"," return \"OpenAI\""," if name.startswith(\"o\"):"," return \"Other\""," df[\"provider\"] = df[\"Model\"].apply(extract_provider)"," # Calculate costs for each row"," df = calculate_costs(df, cost_config, config)"," # Create leaderboard data"," leaderboard = df.groupby(\"Model\").agg({"," \"Success_Rate\": \"mean\","," \"Duration\": \"mean\","," \"total_tokens\": \"mean\","," \"total_cost\": \"mean\","," \"input_cost\": \"mean\","," \"output_cost\": \"mean\","," \"Case\": \"count\", # Number of runs"," \"provider\": \"first\""," }).reset_index()"," leaderboard.columns = [\"Model\", \"Success_Rate\", \"Avg_Duration\", \"Avg_Tokens\", \"Avg_Cost\", \"Avg_Input_Cost\", \"Avg_Output_Cost\", \"Runs\", \"Provider\"]"," leaderboard = leaderboard.sort_values(\"Success_Rate\", ascending=False)"," # Create data for Pareto frontier plot"," pareto_data = df.groupby(\"Model\").agg({"," \"Metric_request_tokens\": lambda x: x[x > 0].mean() if any(x > 0) else 0,"," \"Metric_response_tokens\": lambda x: x[x > 0].mean() if any(x > 0) else 0"," # Fill any remaining NaN values with 0"," pareto_data = pareto_data.fillna(0)"," # Create test group performance data"," test_groups_data = df.groupby([\"Model\", \"test_group\"]).agg({"," \"Score_MermaidDiagramValid\": \"mean\","," \"Score_UsageLimitNotExceeded\": \"mean\","," \"Score_UsedBothMCPTools\": \"mean\","," \"total_tokens\": \"mean\""," # Calculate failure analysis data"," failure_analysis_data = calculate_failure_analysis_data(df)"," # Calculate cost breakdown by model and test group"," cost_breakdown_data = df.groupby([\"Model\", \"test_group\"]).agg({"," \"total_cost\": [\"mean\", \"sum\", \"count\"],"," \"input_cost\": [\"mean\", \"sum\"],"," \"output_cost\": [\"mean\", \"sum\"]"," }).round(6)"," # Flatten the multi-level columns"," cost_breakdown_data.columns = ['_'.join(col).strip() for col in cost_breakdown_data.columns.values]"," cost_breakdown_data = cost_breakdown_data.reset_index()"," cost_breakdown_data = cost_breakdown_data.rename(columns={"," \"total_cost_mean\": \"avg_total_cost\","," \"total_cost_sum\": \"sum_total_cost\", "," \"total_cost_count\": \"run_count\","," \"input_cost_mean\": \"avg_input_cost\","," \"input_cost_sum\": \"sum_input_cost\","," \"output_cost_mean\": \"avg_output_cost\","," \"output_cost_sum\": \"sum_output_cost\""," })"," # Calculate aggregate statistics"," stats = {"," \"total_runs\": len(df),"," \"models_evaluated\": df[\"Model\"].nunique(),"," \"test_cases\": df[\"Case\"].nunique(),"," \"test_groups\": sorted(df[\"test_group\"].unique().tolist()),"," \"providers\": sorted(df[\"provider\"].unique().tolist()),"," \"models\": sorted(df[\"Model\"].unique().tolist()),"," \"total_cost\": df[\"total_cost\"].sum(),"," \"avg_cost_per_run\": df[\"total_cost\"].mean()"," # Create final data structure"," output_data = {"," \"stats\": stats,"," \"leaderboard\": leaderboard.to_dict(orient=\"records\"),"," \"pareto_data\": pareto_data.to_dict(orient=\"records\"),"," \"test_groups_data\": test_groups_data.to_dict(orient=\"records\"),"," \"failure_analysis_data\": failure_analysis_data,"," \"cost_breakdown_data\": cost_breakdown_data.to_dict(\"records\"),"," \"raw_data\": df[["," \"Model\", \"Case\", \"test_group\", \"Duration\", "," \"Score_MermaidDiagramValid\", \"Score_UsageLimitNotExceeded\", "," \"Score_UsedBothMCPTools\", \"total_tokens\", \"provider\","," \"Metric_request_tokens\", \"Metric_response_tokens\","," \"total_cost\", \"input_cost\", \"output_cost\""," ]].to_dict(orient=\"records\"),"," \"config\": {"," \"title\": config.title,"," \"description\": config.description,"," \"primary_metric\": {"," \"name\": \"Success_Rate\","," \"label\": \"Success Rate (%)\""," return output_data","def main():"," parser = argparse.ArgumentParser(description=\"Process CSV evaluation results for static site\")"," parser.add_argument(\"-i\", \"--input_csv\", nargs=\"?\", help=\"Path to input CSV file\", default=None)"," parser.add_argument(\"-o\", \"--output_json\", nargs=\"?\", help=\"Path to output JSON file\", default=None)"," args = parser.parse_args()"," current_month = datetime.now().strftime(\"%b\").lower()"," # Set default paths if not provided"," if args.input_csv:"," csv_path = Path(args.input_csv)"," if not csv_path.is_absolute():"," csv_path = project_root / csv_path"," csv_path = project_root / \"mermaid_eval_results\" / \"latest_combined_results.csv\""," if args.output_json:"," output_path = Path(args.output_json)"," if not output_path.is_absolute():"," output_path = project_root / output_path"," output_path = project_root / \"agents_mcp_usage\" / \"evaluations\" / \"mermaid_evals\" / \"results\" / f\"{current_month}_results_processed.json\""," print(f\"Processing {csv_path}...\")"," data = process_csv_for_static_site(csv_path)"," # Convert the data to JSON string, replacing NaN with null"," json_str = json.dumps(data, indent=2)"," # Replace NaN values with null for valid JSON"," json_str = json_str.replace(\": NaN\", \": null\")"," # Write output"," with open(output_path, 'w') as f:"," f.write(json_str)"," print(f\"Data processed and saved to {output_path}\")"," print(f\"- Total runs: {data['stats']['total_runs']}\")"," print(f\"- Models evaluated: {data['stats']['models_evaluated']}\")"," print(f\"- Test cases: {data['stats']['test_cases']}\")"," main()",[3248],{"_2587":990,"_2588":3249,"_673":3250,"_2591":57},126,[2928,3251,3252,315,3253,3254,3255,3256,3257,315,3258,3251,315,3259,315,3260,3261,3262,315,3263,2940,3264,2942,3265,315,315,3266,3267,3268,3269,3270,3271,315,315,3266,3272,3273,3274,3275,3276,315,315,3266,3277,3278,315,315,3279,3280,315,3281,3282,3283,315,3284,3285,3286,3287,3288,3289,315,315,3290,3291,315,315,3292,3293,3294,3295,3051,3296,315,315,3297,3298,3299,3300,315,3301,315,3302,3303,3304,3305,3306,3307,315,3308,3303,3309,3310,3311,3312,3313,315,3314,3303,3315,3310,3311,3316,3317,315,3318,3303,3319,3310,3311,3316,3317,315,3320,3303,3321,2625,3322,3323,3324,3325,315,3326,315,315,2691,3327],"\"\"\"","Offline verification for run-level retry + backoff orchestration.","This script uses a stub Dataset to simulate:","- evaluation timeouts (retryable at run-level)","- report-based retry triggers (e.g. http_error_429, error_ExceptionGroup)","- non-retry conditions (usage_limit_exceeded, agent_timeout)","- fatal conditions (Mermaid validator unavailable)","It patches delays to zero so it runs quickly and deterministically.","from __future__ import annotations","import asyncio","from dataclasses import dataclass, field","from typing import Any, List, Sequence","import agents_mcp_usage.evaluations.mermaid_evals.run_multi_evals as run_multi_evals"," MermaidValidatorUnavailableError,","from agents_mcp_usage.evaluations.mermaid_evals.run_multi_evals import MultiModelEvaluator","@dataclass(slots=True)","class StubOutput:"," failure_reason: str = \"\""," fixed_diagram: str = \"\""," metrics: dict[str, Any] = field(default_factory=dict)"," tools_used: list[str] = field(default_factory=list)","class StubCase:"," name: str"," task_duration: float = 0.0"," output: StubOutput = field(default_factory=StubOutput)"," scores: dict[str, Any] = field(default_factory=dict)","class StubReport:"," cases: list[StubCase]","class StubDataset:"," \"\"\"A minimal stub that matches the Dataset.evaluate() shape we need.\"\"\""," def __init__(self, sequence: Sequence[object]) -> None:"," self.sequence = list(sequence)"," self.calls = 0"," async def evaluate(self, *_args: Any, **_kwargs: Any) -> object:"," item = self.sequence[self.calls]"," self.calls += 1"," if isinstance(item, BaseException):"," raise item"," return item","async def _no_sleep(_delay: float) -> None:"," return None","def _make_report(failure_reasons: List[str]) -> StubReport:"," cases = ["," StubCase(name=f\"case_{idx}\", output=StubOutput(failure_reason=reason))"," for idx, reason in enumerate(failure_reasons)"," return StubReport(cases=cases)","async def main() -> None:"," # Patch delay + sleep so retries are fast in this script"," run_multi_evals.compute_exponential_backoff_delay = lambda *_a, **_k: 0.0 # type: ignore[assignment]"," run_multi_evals.asyncio.sleep = _no_sleep # type: ignore[assignment]"," model = \"test-model\""," # 1) Evaluation timeout should retry up to MAX_RETRY_ATTEMPTS, then fail."," evaluator = MultiModelEvaluator(models=[model], judge_model=\"judge\", output_dir=\"./mermaid_eval_results\")"," dataset = StubDataset([asyncio.TimeoutError()] * run_multi_evals.MAX_RETRY_ATTEMPTS)"," result = await evaluator.run_single_evaluation(model, 0, dataset, timeout=0.01)"," assert result is None"," assert evaluator.results[model].failed_runs[0][\"attempts\"] == run_multi_evals.MAX_RETRY_ATTEMPTS"," # 2) Retryable report failure_reason should trigger a retry, then succeed."," dataset = StubDataset([_make_report([\"http_error_429\"]), _make_report([\"\"])])"," result = await evaluator.run_single_evaluation(model, 0, dataset, timeout=1)"," assert result is not None"," assert result.attempts == 2"," assert dataset.calls == 2"," # 3) usage_limit_exceeded should NOT trigger a retry."," dataset = StubDataset([_make_report([\"usage_limit_exceeded\"]), _make_report([\"\"])])"," assert result.attempts == 1"," assert dataset.calls == 1"," # 4) agent_timeout should NOT trigger a retry."," dataset = StubDataset([_make_report([\"agent_timeout\"]), _make_report([\"\"])])"," # 5) Mermaid validator unavailability should fail-fast (raise)."," dataset = StubDataset([MermaidValidatorUnavailableError(\"validator down\")])"," await evaluator.run_single_evaluation(model, 0, dataset, timeout=1)"," raise AssertionError(\"Expected MermaidValidatorUnavailableError\")"," except MermaidValidatorUnavailableError:"," pass"," print(\"verify_retry_orchestration.py: OK\")"," asyncio.run(main())",[3329],{"_2587":990,"_2588":3330,"_673":3331,"_2591":57},553,[2928,3332,315,3333,3334,3335,3336,315,3337,3338,3339,3340,3341,3342,3343,3344,3345,315,3346,315,3347,3348,3349,315,3347,3348,3350,3351,315,3347,3348,3350,3352,3353,3251,315,3259,315,2929,2596,2931,3354,2932,2933,3355,315,2935,315,3356,2937,315,2943,315,315,3357,315,315,3358,3359,3360,3361,3362,3363,315,315,3364,3365,3366,3367,3368,315,315,3369,3370,3371,3372,3373,3374,315,3375,2621,315,3376,3377,3378,3379,3380,3381,315,3382,3383,3384,315,3385,315,3386,3387,3388,315,3389,3390,3391,3392,315,3393,3394,3395,3396,315,3397,315,3398,3399,3394,3400,3401,3402,315,3403,3394,3404,315,3405,3406,315,3407,3408,3409,3410,3411,3412,3413,3015,3414,315,3415,3416,315,3417,315,315,3418,3419,315,3420,3421,2621,315,3047,3048,3049,3050,3051,315,3422,3423,315,3052,3424,315,3054,3055,3425,315,3058,3426,3427,3428,3429,3411,3430,3060,3061,315,3062,315,3063,315,315,3431,3432,315,3433,3434,2621,315,3422,3435,3436,3437,3438,3439,3440,3441,3442,3443,3444,2733,315,3445,3446,3447,3448,3449,3450,3451,3452,3453,2733,315,3454,3455,3456,3457,3458,3459,3460,3461,3462,3463,3464,3465,3051,315,3466,3080,3467,315,3468,3469,3470,315,3471,3100,315,3472,3140,3141,3142,3143,3144,3145,3146,3147,3473,3149,315,3474,3475,3476,3477,3478,3479,3480,3481,3482,3483,3051,315,3151,315,3484,3485,3486,3487,315,3153,3141,3142,3143,3144,3145,3146,3488,3489,3149,315,3157,315,3490,3491,3492,3493,315,3159,3160,3161,3162,3144,3145,3146,3143,3149,315,3494,3165,315,3495,3167,3168,3169,3496,3171,315,3497,3498,3174,3499,3500,3501,3502,3503,3504,3505,3506,3507,2733,2608,315,3508,3185,3509,3510,3511,3512,3513,3514,3515,3516,2690,315,3517,3518,3475,3519,3520,3456,3457,3458,3459,3460,3521,3461,3462,3463,3464,3465,3051,315,3522,3523,3080,3524,315,3525,315,2681,3196,3197,3198,3199,3200,3526,3527,3528,2690,315,315,3529,3530,3531,3532,315,3533,3534,3535,3536,3537,2608,315,315,3538,3539,3540,3541,3542,3543,3397,315,3544,3545,3546,315,3547,3548,315,3549,3550,3551,3552,3553,3554,3555,3556,3557,3121,3558,2778,3559,3560,3561,3562,3563,3564,2730,3565,2690,315,3566,315,315,3567,3568,3569,2608,315,3570,3571,3572,3573,3574,3575,3576,2608,315,3570,3577,3578,3579,3575,3580,2608,315,3570,3581,3582,3583,3584,3585,3586,3587,3588,2608,315,3570,3589,3590,3591,2608,315,3570,3592,3593,3594,2608,315,3595,315,315,3596,3597,315,3069,315,3598,3599,3600,3601,3602,3603,3604,315,3605,3606,3607,315,3608,3609,3610,3611,3612,315,3613,3614,3615,3616,3617,3618,3619,3620,3621,315,3622,3623,3624,315,3625,3626,3627,3628,315,3629,3630,3631,3632,315,3633,3634,3635,315,3613,3636,3637,315,3638,315,3639,3640,3641,3642,315,3613,3643,315,3644,3645,3646,3647,3648,3649,3650,2653,315,3651,3652,3653,315,3654,3655,315,315,3656,3246],"\"\"\"Merge multiple Merbench processed JSON result files.","This script combines one or more JSON files produced by","`preprocess_merbench_data.py`, optionally deduplicates runs for the","same (Model, Case) combination using different strategies, and then","recomputes all aggregate sections from the merged raw data.","The output JSON has the same schema as a single processed file:","- stats","- leaderboard","- pareto_data","- test_groups_data","- failure_analysis_data","- cost_breakdown_data","- raw_data","- config","Usage examples (as documented in README):"," python scripts/merge_benchmark_results.py \\"," -i file1.json file2.json \\"," -o merged.json"," -o merged.json \\"," --dedup keep-first"," --report merge_report.json \\"," --verbose","from dataclasses import dataclass","from typing import Any, Dict, Iterable, List, Literal, Optional","# Add parent directory to path to import project modules (mirrors preprocess script)","DedupStrategy = Literal[\"keep-all\", \"keep-first\", \"keep-last\", \"average\"]","@dataclass","class InputFileSummary:"," path: str"," total_runs: int"," models: List[str]"," test_cases: List[str]","def load_json_file(path: Path, verbose: bool = False) -> Dict[str, Any]:"," if verbose:"," print(f\"Loading {path}...\")"," with path.open(\"r\", encoding=\"utf-8\") as f:"," return json.load(f)","def merge_raw_data("," datasets: Iterable[Dict[str, Any]],"," dedup_strategy: DedupStrategy = \"keep-all\","," verbose: bool = False,",") -> pd.DataFrame:"," \"\"\"Merge raw_data arrays from multiple processed JSON datasets."," Deduplication is performed on (Model, Case) pairs when requested."," all_rows: List[Dict[str, Any]] = []"," for data in datasets:"," raw = data.get(\"raw_data\", [])"," if not isinstance(raw, list):"," raise ValueError(\"Each input JSON must contain a 'raw_data' list.\")"," all_rows.extend(raw)"," if not all_rows:"," # Return empty DataFrame with no rows"," return pd.DataFrame()"," df_all = pd.DataFrame(all_rows)"," # First, drop exact duplicate rows across all columns to avoid double-counting"," # when the same processed JSON is merged multiple times."," df_all = df_all.drop_duplicates().reset_index(drop=True)"," # Ensure key columns exist"," for col in [\"Model\", \"Case\"]:"," if col not in df_all.columns:"," raise KeyError(f\"Expected column '{col}' to be present in raw_data\")"," if dedup_strategy == \"keep-all\":"," if verbose:"," print(\"Deduplication strategy 'keep-all': keeping all unique rows (no per-(Model, Case) deduplication).\")"," return df_all.reset_index(drop=True)"," key_cols = [\"Model\", \"Case\"]"," if dedup_strategy in {\"keep-first\", \"keep-last\"}:"," keep_arg = \"first\" if dedup_strategy == \"keep-first\" else \"last\""," print(f\"Deduplication strategy '{dedup_strategy}': dropping duplicates, keeping {keep_arg}.\")"," df_dedup = df_all.drop_duplicates(subset=key_cols, keep=keep_arg).reset_index(drop=True)"," return df_dedup"," if dedup_strategy == \"average\":"," print(\"Deduplication strategy 'average': averaging numeric fields for duplicate (Model, Case) pairs.\")"," # Identify numeric vs non-numeric columns"," numeric_cols = df_all.select_dtypes(include=\"number\").columns.tolist()"," agg_dict = {}"," for col in df_all.columns:"," if col in key_cols:"," # Key columns are included in groupby, no aggregation spec needed"," continue"," if col in numeric_cols:"," agg_dict[col] = \"mean\""," agg_dict[col] = \"first\""," grouped = df_all.groupby(key_cols, as_index=False).agg(agg_dict)"," return grouped.reset_index(drop=True)"," raise ValueError(f\"Unknown deduplication strategy: {dedup_strategy}\")","def calculate_failure_analysis_data(df: pd.DataFrame) -> List[Dict[str, Any]]:"," \"\"\"Calculate failure counts by model and failure type."," This mirrors the logic in preprocess_merbench_data.calculate_failure_analysis_data,"," but operates on the merged DataFrame."," if df.empty:"," return []"," failure_data: List[Dict[str, Any]] = []"," failure_counts: Dict[str, Any] = {\"Model\": model}"," column = series[\"column\"]"," if column not in model_data.columns:"," # If a score column is missing entirely, treat as zero failures"," failure_counts[series[\"name\"]] = 0"," condition_str = f\"`{column}` {series['condition']}\"","def build_aggregates(df: pd.DataFrame, config: Dict[str, Any]) -> Dict[str, Any]:"," \"\"\"Build all aggregate sections from merged raw_data DataFrame."," This mirrors the logic in preprocess_merbench_data.process_csv_for_static_site,"," but assumes costs and tokens are already computed in raw_data."," # Return an empty-but-well-formed structure"," stats = {"," \"total_runs\": 0,"," \"models_evaluated\": 0,"," \"test_cases\": 0,"," \"test_groups\": [],"," \"providers\": [],"," \"models\": [],"," \"total_cost\": 0.0,"," \"avg_cost_per_run\": 0.0,"," return {"," \"stats\": stats,"," \"leaderboard\": [],"," \"pareto_data\": [],"," \"test_groups_data\": [],"," \"failure_analysis_data\": [],"," \"cost_breakdown_data\": [],"," \"raw_data\": [],"," \"config\": config or {},"," # Ensure expected columns exist; fill missing numeric columns with 0"," numeric_defaults = ["," \"Duration\","," \"Score_MermaidDiagramValid\","," \"Score_UsageLimitNotExceeded\","," \"Score_UsedBothMCPTools\","," \"total_tokens\","," \"Metric_request_tokens\","," \"Metric_response_tokens\","," \"total_cost\","," \"input_cost\","," \"output_cost\","," for col in numeric_defaults:"," df[col] = 0.0"," # Fill NaN with 0 for numeric columns"," numeric_columns = df.select_dtypes(include=\"number\").columns"," df[numeric_columns] = df[numeric_columns].fillna(0)"," # Derive Success_Rate as in preprocess script"," # Leaderboard data (group by Model)"," \"provider\": \"first\","," leaderboard.columns = ["," \"Model\","," \"Success_Rate\","," \"Avg_Duration\","," \"Avg_Tokens\","," \"Avg_Cost\","," \"Avg_Input_Cost\","," \"Avg_Output_Cost\","," \"Runs\","," \"Provider\","," # Pareto data (group by Model)"," def _nonzero_mean(series: pd.Series) -> float:"," non_zero = series[series > 0]"," return float(non_zero.mean()) if not non_zero.empty else 0.0"," \"Metric_request_tokens\": _nonzero_mean,"," \"Metric_response_tokens\": _nonzero_mean,"," # Test group performance data (group by Model + test_group)"," if \"test_group\" not in df.columns:"," # Derive a default test_group if missing"," df[\"test_group\"] = \"other\""," # Failure analysis data"," # Cost breakdown data (group by Model + test_group)"," \"output_cost\": [\"mean\", \"sum\"],"," # Flatten multi-level columns"," cost_breakdown_data.columns = [\"_\".join(col).strip() for col in cost_breakdown_data.columns.values]"," cost_breakdown_data = cost_breakdown_data.rename("," columns={"," \"total_cost_mean\": \"avg_total_cost\","," \"total_cost_sum\": \"sum_total_cost\","," \"total_cost_count\": \"run_count\","," \"input_cost_mean\": \"avg_input_cost\","," \"input_cost_sum\": \"sum_input_cost\","," \"output_cost_mean\": \"avg_output_cost\","," \"output_cost_sum\": \"sum_output_cost\","," # Aggregate statistics from merged raw data"," \"total_runs\": int(len(df)),"," \"models_evaluated\": int(df[\"Model\"].nunique()),"," \"test_cases\": int(df[\"Case\"].nunique()),"," \"test_groups\": sorted(df[\"test_group\"].dropna().unique().tolist()),"," \"providers\": sorted(df[\"provider\"].dropna().unique().tolist()) if \"provider\" in df.columns else [],"," \"models\": sorted(df[\"Model\"].dropna().unique().tolist()),"," \"total_cost\": float(df[\"total_cost\"].sum()),"," \"avg_cost_per_run\": float(df[\"total_cost\"].mean() if len(df) > 0 else 0.0),"," # Raw data to records (ensure stable column subset as per schema)"," raw_columns = ["," \"Case\","," \"test_group\","," \"provider\","," # Add any missing columns in the expected raw_data schema"," for col in raw_columns:"," df[col] = 0 if col in numeric_defaults else None"," raw_data_records = df[raw_columns].to_dict(orient=\"records\")"," \"cost_breakdown_data\": cost_breakdown_data.to_dict(orient=\"records\"),"," \"raw_data\": raw_data_records,"," \"config\": config or {},","def summarise_input_file(path: Path, data: Dict[str, Any]) -> InputFileSummary:"," raw = data.get(\"raw_data\", [])"," models = sorted({row.get(\"Model\", \"\") for row in raw if \"Model\" in row})"," cases = sorted({row.get(\"Case\", \"\") for row in raw if \"Case\" in row})"," return InputFileSummary("," path=str(path),"," total_runs=len(raw),"," models=models,"," test_cases=cases,","def build_merge_report("," input_summaries: List[InputFileSummary],"," dedup_strategy: DedupStrategy,"," df_all: pd.DataFrame,"," df_merged: pd.DataFrame,",") -> Dict[str, Any]:"," total_input_runs = int(len(df_all))"," unique_runs_before = int(df_all.drop_duplicates(subset=key_cols).shape[0]) if not df_all.empty else 0"," unique_runs_after = int(df_merged.drop_duplicates(subset=key_cols).shape[0]) if not df_merged.empty else 0"," duplicates_before = total_input_runs - unique_runs_before"," duplicates_removed = unique_runs_before - unique_runs_after"," summary = {"," \"merged_at\": datetime.utcnow().isoformat() + \"Z\","," \"dedup_strategy\": dedup_strategy,"," \"input_files\": ["," {"," \"path\": s.path,"," \"total_runs\": s.total_runs,"," \"models\": s.models,"," \"test_cases\": s.test_cases,"," for s in input_summaries"," \"summary\": {"," \"total_input_runs\": total_input_runs,"," \"unique_runs_before_dedup\": unique_runs_before,"," \"unique_runs_after_dedup\": unique_runs_after,"," \"duplicate_runs_detected\": duplicates_before,"," \"duplicate_runs_removed\": max(0, duplicates_removed),"," \"models_merged\": sorted(df_merged[\"Model\"].dropna().unique().tolist()) if not df_merged.empty else [],"," return summary","def parse_args(argv: Optional[List[str]] = None) -> argparse.Namespace:"," parser = argparse.ArgumentParser("," description=\"Merge multiple Merbench processed JSON result files.\","," parser.add_argument("," \"-i\","," \"--input\","," nargs=\"+\","," dest=\"input_files\","," required=True,"," help=\"Input JSON files produced by preprocess_merbench_data.py\","," \"-o\","," \"--output\","," dest=\"output_file\","," help=\"Path to write merged JSON output\","," \"--dedup\","," dest=\"dedup_strategy\","," choices=[\"keep-all\", \"keep-first\", \"keep-last\", \"average\"],"," default=\"keep-all\","," help=("," \"Deduplication strategy for duplicate (Model, Case) combinations: \""," \"keep-all (default), keep-first, keep-last, average\""," ),"," \"--report\","," dest=\"report_file\","," help=\"Optional path to write a detailed merge report JSON\","," \"--verbose\","," action=\"store_true\","," help=\"Enable verbose logging\","," return parser.parse_args(argv)","def main(argv: Optional[List[str]] = None) -> None:"," args = parse_args(argv)"," # Resolve file paths relative to project root if not absolute"," input_paths: List[Path] = []"," for p in args.input_files:"," path = Path(p)"," if not path.is_absolute():"," path = project_root / path"," input_paths.append(path)"," output_path = Path(args.output_file)"," if not output_path.is_absolute():"," output_path = project_root / output_path"," report_path: Optional[Path] = None"," if args.report_file:"," report_path = Path(args.report_file)"," if not report_path.is_absolute():"," report_path = project_root / report_path"," if args.verbose:"," print(\"Merging Merbench benchmark results...\")"," print(f\"- Deduplication strategy: {args.dedup_strategy}\")"," print(\"- Input files:\")"," for p in input_paths:"," print(f\" - {p}\")"," print(f\"- Output file: {output_path}\")"," if report_path:"," print(f\"- Report file: {report_path}\")"," # Load all datasets"," datasets: List[Dict[str, Any]] = []"," input_summaries: List[InputFileSummary] = []"," for path in input_paths:"," data = load_json_file(path, verbose=args.verbose)"," datasets.append(data)"," input_summaries.append(summarise_input_file(path, data))"," # Choose a canonical config from the first dataset (if any)"," config: Dict[str, Any] = {}"," if datasets and isinstance(datasets[0].get(\"config\"), dict):"," config = datasets[0][\"config\"]"," # Merge raw_data and build aggregates"," df_all = pd.DataFrame([row for data in datasets for row in data.get(\"raw_data\", [])])"," df_merged = merge_raw_data(datasets, dedup_strategy=args.dedup_strategy, verbose=args.verbose)"," print(f\"Total input runs: {len(df_all)}\")"," print(f\"Merged runs after dedup ({args.dedup_strategy}): {len(df_merged)}\")"," merged_data = build_aggregates(df_merged, config=config)"," # Write merged JSON output"," output_path.parent.mkdir(parents=True, exist_ok=True)"," with output_path.open(\"w\", encoding=\"utf-8\") as f:"," json.dump(merged_data, f, indent=2)"," print(f\"Merged results written to {output_path}\")"," # Optionally write a merge report"," if report_path is not None:"," report = build_merge_report("," input_summaries=input_summaries,"," dedup_strategy=args.dedup_strategy,"," df_all=df_all,"," df_merged=df_merged,"," report_path.parent.mkdir(parents=True, exist_ok=True)"," with report_path.open(\"w\", encoding=\"utf-8\") as f:"," json.dump(report, f, indent=2)"," if args.verbose:"," print(f\"Merge report written to {report_path}\")","if __name__ == \"__main__\": # pragma: no 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Mermaid Diagram Evaluation System","A benchmarking system for evaluating LLM agents' ability to fix syntactically incorrect Mermaid diagrams using Model Context Protocol (MCP) servers.","**Key Features:**","- 🎯 Tests three difficulty levels of invalid Mermaid diagrams","- 🔧 Utilises multiple MCP servers for validation and syntax errors","- 📊 Comprehensive evaluation metrics and scoring","- 🏆 Live leaderboard at ","## Architecture","```mermaid","flowchart TD"," Runner((\"run_multi_evals.py
or
evals_pydantic_mcp.py\"))"," Agent[\"Pydantic-AI Agent\"]"," MCP[\"MCP Servers\"]"," LLM[\"LLM Provider\"]"," Evaluators[\"Evaluators\"]"," CSV[\"CSV Results\"]"," Dashboard[\"merbench_ui.py
(Local Dashboard)\"]"," Script[\"preprocess_merbench_data.py\"]"," JSON[\"JSON for Merbench\"]"," Runner --> Agent"," Agent --> MCP"," Agent --> LLM"," Runner --> Evaluators"," Evaluators --> CSV"," CSV --> Dashboard"," CSV -.-> Script"," Script -.-> JSON"," style Dashboard fill:#9cf,stroke:#36f,stroke-width:2px","```","## Quick Overview","* **Test cases**: 3 invalid Mermaid diagrams (easy/medium/hard)","* **Success metric**: Diagram syntax validity (100% = all cases pass)","* **Evaluation runners**: "," - `evals_pydantic_mcp.py` - Single-model evaluation"," - `run_multi_evals.py` - Multi-model parallel evaluation","* **Results visualisation**: "," - `merbench_ui.py` - Local Streamlit dashboard (recommended)"," - CSV → JSON → Public web dashboard (optional)","* **Performance scoring**: Based solely on diagram validity (LLMJudges implemented but not scored)","## Prerequisites & Setup","### Requirements","- Python 3.11+","- [`uv`](https://github.com/astral-sh/uv) for dependency management","### Installation","```bash","# Install all dependencies","uv sync","### Configuration","The evaluation system uses Pydantic-AI and requires environment variables for API keys:","# Required for most models","export GEMINI_API_KEY=\"your-key-here\"","# Optional for specific providers/models","export OPENAI_API_KEY=\"your-key-here\"","export OPENROUTER_API_KEY=\"your-key-here\" # required for `openrouter:*` models","## Quick Start Guide","### 1. Run a Single-Model Evaluation","# Test a specific model (defaults to gemini-2.5-flash)","uv run agents_mcp_usage/evaluations/mermaid_evals/evals_pydantic_mcp.py","### 2. Run Multi-Model Benchmarking","# Run evaluation across multiple models in parallel","uv run agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py","# Example: include an OpenRouter model (requires OPENROUTER_API_KEY)","uv run agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py \\"," --models \"openrouter:anthropic/claude-3.7-sonnet,gemini-3-flash-preview\" \\"," --runs 5 \\"," --sequential","### 3. View Results in Local Dashboard (Recommended)","# Launch the interactive Streamlit dashboard to visualise your results","make leaderboard","# Or run directly:","uv run streamlit run agents_mcp_usage/evaluations/mermaid_evals/merbench_ui.py","The local dashboard (`merbench_ui.py`) provides:","- 📊 Interactive leaderboards and performance metrics","- 📈 Pareto frontier visualisations (performance vs cost/tokens)","- 🔍 Deep dive analysis with success rates and failure patterns","- 💰 Configurable model costs","- 🎯 Advanced filtering by provider, model, and test case","### 4. Export Results for Web Dashboard (Optional)","# Convert CSV results to JSON format for the public Merbench website","uv run agents_mcp_usage/evaluations/mermaid_evals/scripts/preprocess_merbench_data.py \\"," -i \"mermaid_eval_results/_combined_results.csv\" \\"," -o \"agents_mcp_usage/evaluations/mermaid_evals/results/_processed.json\"","### 5. Merge Results from Multiple Runs (Optional)","# Combine results from different machines or benchmark runs","uv run agents_mcp_usage/evaluations/mermaid_evals/scripts/merge_benchmark_results.py \\"," -i results/run1.json results/run2.json \\"," -o results/merged.json \\"," --dedup keep-all \\"," --report merge_report.json","## Evaluation Task & Test Cases","The system challenges LLM agents to:","1. **Identify and fix syntax errors** in invalid Mermaid diagrams","2. **Utilise MCP servers** for validation and enhancement","3. **Handle errors gracefully** with proper categorisation","4. **Provide meaningful failure reasons** for debugging","### Test Case Difficulty Levels","1. **Easy** (2 syntax errors)"," - Incorrect node references (`GEMINI` vs `GEM`)"," - Minor naming inconsistencies"," - Example: `MCP --> GEMINI` where `GEMINI` is undefined","2. **Medium** (7 syntax errors)"," - Invalid comment syntax (using `#` instead of `%%`)"," - Malformed arrows with spaces (`-- >` instead of `-->`)"," - Direction inconsistencies (`TB` vs `TD`)"," - Example: `LG -- > MCP` has invalid spacing in arrow","3. **Hard** (Complex structural errors)"," - Multiple comment syntax errors"," - Arrow spacing issues throughout"," - Complex nested subgraph problems"," - Requires understanding of overall diagram structure","### Example Error Types","%% Invalid examples from actual test cases:","graph LR"," %% Error 1: Space in arrow"," A -- > B %% Should be: A --> B"," %% Error 2: Undefined node reference"," C --> UNDEFINED_NODE %% Node doesn't exist"," %% Error 3: Wrong comment syntax"," # This is wrong %% Should use %%","## Output Schema & Metrics","### MermaidOutput Schema","```python","class MermaidOutput(BaseModel):"," fixed_diagram: str # The corrected mermaid diagram"," failure_reason: str = \"\" # Why the case failed (if applicable)"," metrics: Dict[str, Any] = {} # Usage metrics from the LLM"," tools_used: List[str] = [] # Which MCP tools were called","### Captured Metrics","- **`requests`** - Number of API requests made","- **`request_tokens`** - Total tokens in requests","- **`response_tokens`** - Total tokens in responses","- **`total_tokens`** - Sum of request and response tokens","- **`details`** - Additional model-specific usage details","## Evaluation Criteria","### 1. **MermaidDiagramValid** (Primary Score)","- **Weight**: 100% of final score","- **Purpose**: Validates diagram syntax using MCP server","- **Score**: 1.0 (valid) or 0.0 (invalid)","### 2. **UsedBothMCPTools**","- **Purpose**: Tracks MCP server utilisation","- **Scoring**:"," - 1.0: Used both servers (example + validator)"," - 0.5: Used only one server"," - 0.0: No MCP tool usage","### 3. **UsageLimitNotExceeded**","- **Purpose**: Detects token limit failures","- **Score**: 1.0 (success) or 0.0 (limit exceeded)","### 4. **LLMJudge Evaluators** (Not scored)","- **Format Check**: Response contains only Mermaid diagram","- **Structure Check**: Maintains original diagram intent","## MCP Servers","### 1. Example Server (`mcp_servers/example_server.py`)","- **Purpose**: Provides utility tools","- **Features**: Time functions for adding timestamps","### 2. Mermaid Validator (`mcp_servers/mermaid_validator.py`)","- **Purpose**: Validates Mermaid syntax","- **Features**: Returns detailed error messages","## Error Handling","### Failure Categories","**Agent-level failures:**","- `usage_limit_exceeded` - Token limit reached","- `response_validation_failed` - Invalid output format","- `agent_timeout` - Execution timeout","- `http_error_*` - HTTP status errors","- `connection_error` - Network issues","**Evaluation-level failures:**","- `evaluation_timeout` - Overall timeout","- `model_api_error` - API authentication/access","- `evaluation_error_{ExceptionType}` - Unexpected errors","### Retry Strategy","- **Attempts**: 3 retries for transient errors","- **Backoff**: Exponential (1s → 2s → 4s)","- **Jitter**: ±50% randomisation","- **Max delay**: 30 seconds","- **Retryable**: HTTP 429/5xx, network errors, OSError","- **Non-retryable**: HTTP 4xx (except 429), validation errors","## Output Files","### Directory Structure","mermaid_eval_results/ # Default output directory","├── YYYY-MM-DD_HH-MM-SS_mermaid_results_{model}.csv # Single model","├── YYYY-MM-DD_HH-MM-SS_individual_{model}.csv # Multi-model individual","└── YYYY-MM-DD_HH-MM-SS_combined_results.csv # Multi-model combined","The local dashboard (`merbench_ui.py`) automatically detects and loads these CSV files from the `mermaid_eval_results/` directory.","### File Contents","- Test case details and inputs","- Fixed diagrams and validation results","- Performance metrics and scores","- Error messages and failure reasons","## Merging Benchmark Results","The `merge_benchmark_results.py` script enables combining multiple JSON benchmark result files generated by `preprocess_merbench_data.py`. This is particularly useful when:","- Running benchmarks on different machines","- Combining results from different time periods","- Aggregating data from distributed benchmark runs","### Features","- **Multiple merge strategies** for handling duplicate test runs","- **Complete recalculation** of all statistics from merged raw data","- **Detailed merge reports** showing what was combined","- **Preservation of all data sections** (leaderboard, failure analysis, cost breakdown, etc.)","### Usage Examples","#### Basic Merge","python scripts/merge_benchmark_results.py -i file1.json file2.json -o merged.json","#### With Deduplication Strategy","python scripts/merge_benchmark_results.py \\"," -i file1.json file2.json \\"," -o merged.json \\"," --dedup keep-first","#### Generate Detailed Report"," --report merge_report.json \\"," --verbose","### Deduplication Strategies","The script offers four strategies for handling duplicate (Model, Case) combinations:","1. **`keep-all`** (default)"," - Keeps all records, no deduplication"," - Use when runs were performed under different conditions"," - Preserves complete data for analysis","2. **`keep-first`**"," - Keeps the first occurrence from the first file"," - Use when preferring older/original results"," - Maintains consistency with initial benchmarks","3. **`keep-last`**"," - Keeps the last occurrence from the last file"," - Use when preferring newer/updated results"," - Good for iterative improvements","4. **`average`**"," - Averages metrics for duplicate combinations"," - Use when multiple runs should be combined statistically"," - Provides a balanced view across multiple executions","### Output Structure","The merged JSON file maintains the same structure as individual result files:","- `stats`: Aggregate statistics","- `leaderboard`: Model performance rankings","- `pareto_data`: Efficiency analysis data","- `test_groups_data`: Performance by test difficulty","- `failure_analysis_data`: Failure type counts","- `cost_breakdown_data`: Cost analysis by model and test group","- `raw_data`: Individual run records","- `config`: Dashboard configuration","### Merge Report","When using `--report`, the script generates a detailed report containing:","- Timestamp of merge operation","- Deduplication strategy used","- Details of each input file (runs, models)","- Summary statistics (total runs, duplicates handled)","- Complete list of merged models","### Complete Workflow Example","#### 1. Run benchmarks on Machine A:","# Run evaluation","# Convert to JSON"," -i mermaid_eval_results/machine_a_results.csv \\"," -o results/machine_a.json","#### 2. Run benchmarks on Machine B:"," -i mermaid_eval_results/machine_b_results.csv \\"," -o results/machine_b.json","#### 3. Merge results:"," -i results/machine_a.json results/machine_b.json \\"," -o results/combined.json \\"," --report results/merge_summary.json","### Notes","- The script automatically recalculates all statistics from the merged raw data","- Cost calculations use the same `costs.json` configuration as the preprocessing script","- Provider detection is based on model name patterns (gemini→Google, nova→Amazon, etc.)","## Monitoring & Debugging","### Logfire (remote, if configured)","All evaluation runs are traced with **Logfire** for comprehensive monitoring:","- Tool call traces","- Retry attempts and reasons","- Execution durations","- Categorised failure analysis","### Local per-case debug traces (JSON)","When diagnosing failure modes across models (e.g. tool-call loops, request/turn limits, redundant tool usage), you can enable a local debug mode that writes a detailed per-case trace JSON file for each model attempt.","Enable traces:"," --runs 1 \\"," --sequential \\"," --debug-traces","Optional: choose where traces are written:"," --debug-traces \\"," --trace-dir ./mermaid_eval_results/debug_traces","By default, the trace directory is:","- `/debug_traces/` (where `` defaults to `./mermaid_eval_results`)","#### Output layout","Traces are stored under a per-model subdirectory:","```text","/"," /"," model=__run###__attempt###__case=.json","> `run###` is the planned run number (1-based), `attempt###` is the runner-level retry attempt (1-based), and `case_slug` is derived from the case name (or a hash of the input if absent).","#### What gets captured","Each trace JSON file includes (at minimum):","- input: invalid diagram + hash + case name","- prompt query sent to the agent","- full message history (including tool call args and tool return payloads)","- derived message summary:"," - request count, response count"," - tool call/return counts"," - tool name sequence (helps spot loops)","- usage limits used for the run","- usage/tokens (when available from the provider)","- output text and extracted mermaid diagram (on success)","- exception metadata and traceback (on failures)","**Important:** traces contain full prompts and full tool return payloads. Treat them as sensitive and avoid sharing publicly.","## Troubleshooting","### Common Issues","1. **API Key Errors**"," - Ensure environment variables are set correctly"," - Check API key permissions and quotas","2. **MCP Server Connection Issues**"," - Verify MCP servers are properly installed"," - Check server process logs","3. **Validation Failures**"," - Review the fixed diagram for syntax errors"," - Check Mermaid version compatibility","4. **Performance Issues**"," - Reduce parallel execution for rate limits"," - Monitor token usage per model","## Advanced Usage","### Custom Model Configuration","You can customise model parameters when running evaluations:","# Single model","# Edit `evals_pydantic_mcp.py` to customise the `agent_model` string","# Multi-model with specific model list","# Edit `run_multi_evals.py` to customise the `MODELS` list","### Running Specific Test Cases","To test individual cases during development:","1. Import specific test cases from `mermaid_diagrams.py`","2. Modify the test loop in `evals_pydantic_mcp.py`","3. Focus on debugging specific error types","### Adding New Test Cases","1. Add your invalid diagram to `mermaid_diagrams.py`","2. Document the specific errors it contains","3. Ensure it has a corresponding valid version","4. Update the difficulty categorisation","## Contributing","When adding new test cases or evaluators:","1. Follow the existing schema patterns","2. Include comprehensive error handling","3. Add appropriate retry logic","4. Document failure categories","5. Test with multiple models before submitting","6. Update costs.json with new model pricing if needed","## Related Resources","- [Mermaid Documentation](https://mermaid.js.org/)","- [MCP Protocol Specification](https://github.com/anthropics/mcp)","- [Pydantic-AI Documentation](https://github.com/pydantic/pydantic-ai)","- [Streamlit Documentation](https://streamlit.io/) (for local dashboard)","- [Public Leaderboard](https://andrew.ginns.uk/merbench)",[4007],{"_2587":990,"_2588":4008,"_673":4009,"_2591":57},1292,[3251,4010,315,4011,4012,4013,4014,4015,4016,4017,4018,315,4019,3251,315,3260,4020,4021,4022,2596,2592,4023,2930,2931,4024,3354,2932,4025,315,4026,2599,4027,4028,4029,4030,4031,4032,4033,4034,315,4035,4036,4037,4038,4039,2942,4040,4041,2942,4042,315,2601,315,4043,4044,4045,2942,4046,4047,315,4048,4049,315,4050,4051,315,4052,4053,4054,315,4055,4056,4057,4058,315,4059,4060,4061,4062,4063,4064,4065,315,4066,4067,4066,315,315,4068,4069,315,4070,2621,315,315,4071,4072,4073,4074,4075,3090,4076,315,315,4077,4078,4079,4080,2942,315,315,4081,4082,4083,4084,4085,4086,315,315,4087,4088,4089,4090,4091,4092,4093,4094,4095,4096,4097,315,315,4098,4099,315,4100,4101,315,4102,4103,4104,2621,4083,4105,315,4106,4107,4105,315,4073,4108,315,4109,4110,4111,315,4112,4113,4111,4114,4111,315,4115,4116,4117,4118,4119,4120,4121,4122,4121,315,4123,4084,315,4124,4125,315,4126,4127,4084,315,4128,315,315,4129,4130,4131,4089,4090,4091,4132,4133,4134,315,4135,4136,315,2617,4137,4138,4139,4140,4141,315,2619,4142,315,4143,4144,2621,4145,315,4146,3003,4147,4148,4149,315,4150,4151,4152,4153,4154,4155,4156,4157,315,4158,4159,4160,4153,4154,4161,4156,4162,315,4163,4164,2659,315,4165,4166,4167,4168,4169,4170,4171,2659,315,4172,315,4173,4174,4175,315,4176,4177,315,315,4066,4178,4066,315,315,4179,4180,315,4181,4182,315,2619,4183,2621,4184,4185,4186,4187,4188,4189,2778,2608,4190,4185,4186,4187,4191,2778,2608,4192,315,315,4193,4194,4195,4196,315,4197,4198,315,2617,4199,4200,315,2619,4201,2621,4202,4203,315,4204,4205,4206,4207,4208,2608,315,315,4066,4209,4066,315,315,4210,4211,315,4212,4213,315,315,3783,4214,315,4215,4216,4217,4218,315,315,4066,4219,4066,315,4220,315,315,4221,4222,4223,315,315,4224,4225,315,315,4226,4227,4228,4229,4230,4231,4232,4233,4234,4235,4236,4237,4238,315,315,4239,4240,4241,2681,4242,4243,4244,4245,4246,2690,315,315,4247,4248,4249,4250,4251,2690,315,4124,4252,3553,4253,4254,4255,3121,2653,315,4073,4256,315,4238,315,315,4257,4258,315,4259,2621,4260,3042,315,4261,4262,4263,4264,4265,4266,4267,4268,4269,2690,315,4270,4271,315,4272,4273,2997,315,4274,4275,4276,4277,4278,315,4279,4280,2997,315,4281,4282,3411,4283,4284,4285,315,4286,4287,315,4288,4289,4290,4291,4292,4293,4156,315,4294,4295,4292,4296,4156,315,4297,4298,3566,315,315,3266,4299,4300,315,4301,4302,4303,4304,4305,4306,315,315,4307,4308,4309,4310,4311,4312,4313,2608,4314,4315,4316,4317,2608,315,4318,4319,4320,315,315,4321,4322,2625,4323,4324,4325,4326,4327,4328,4329,4330,4331,4332,4333,2653,315,315,4066,4334,4066,315,315,4335,4336,315,4337,4338,4339,4340,315,4341,4342,315,4343,4344,315,4345,4346,4347,2689,4348,4349,315,4350,4351,4352,4353,2653,4354,4355,4353,2653,315,4356,4357,4358,4359,2660,4349,315,315,4360,4361,315,4337,4338,4339,4362,315,4363,4364,315,4343,4365,315,4345,4366,2689,4367,4165,4368,4369,2659,4349,4370,4371,315,315,4372,4373,315,4337,4338,4339,4374,315,4375,4376,315,4343,4365,315,4345,4377,2689,4378,4379,4380,4381,4382,2659,4349,315,4383,4384,315,4385,4386,4387,4388,4389,315,4390,4391,315,4392,4393,4394,4395,2653,315,4396,3003,4397,4398,2659,4399,4400,4148,4401,4402,4167,4168,2659,4403,4404,4405,315,4406,4407,4408,4409,4120,4410,4411,315,4412,4413,2660,4165,4414,2659,315,4415,315,315,4066,4416,4066,315,315,4417,4418,4419,4420,4421,4422,4423,315,4424,4425,4426,4427,315,2617,4428,4199,4429,315,2619,4430,2621,4431,4432,4433,2608,315,4434,315,4435,4436,315,4437,4438,315,4439,4440,4441,315,4442,4443,4444,4445,4446,4447,4448,4449,4450,315,4451,4452,315,4453,315,4454,4455,4456,4457,4458,4459,315,4460,4461,4462,4463,4464,4465,4466,4467,4468,4469,4470,4471,4472,4473,4474,4475,4476,4477,4478,4479,4480,4481,4474,4482,4483,4484,4485,4486,4487,4474,4488,4489,4490,4491,4492,4474,2733,315,4493,4494,315,4495,315,4496,4450,315,4497,4498,4499,2672,4500,4501,4502,4503,4504,4505,4506,4507,4508,4509,4510,4511,4512,4513,4514,4515,4516,4517,4514,4518,4516,4519,315,4520,4521,4522,315,2625,4523,4524,315,4525,4526,4527,315,4528,4529,4530,4531,4532,4290,4533,315,4534,4535,315,4536,4537,4538,4539,4540,2660,4541,315,4542,4543,4544,4545,4546,2653,315,4547,315,4548,4542,4549,4550,4551,4552,4553,2653,4554,315,4555,4329,4556,4557,4558,2653,4542,4559,4560,4551,4552,4553,2653,4561,4562,4560,4563,4564,2653,315,4565,4566,4567,4557,4568,4569,4558,2653,4542,4559,4570,4551,4552,4553,2653,4561,4562,4570,4563,4564,2653,315,4571,4566,4572,4557,4558,2653,4542,4559,4573,4551,4552,4553,2653,4561,4562,4573,4563,4564,2653,315,4574,4566,4575,4557,4558,2653,4542,4559,4576,4551,4552,4553,2653,4561,4562,4576,4563,4564,2653,315,2664,4577,4578,4579,4404,2659,4580,4581,4582,4583,4584,4585,2659,4586,315,4587,4588,4589,315,4590,4591,315,4592,4593,4594,4595,4596,4597,315,4598,4599,4600,4601,4602,3015,4603,4604,4605,4606,4607,4608,4609,4156,4610,4611,4612,4613,4614,4615,4616,4617,4618,4619,2660,4620,4621,4622,4623,4624,4625,4626,3015,4627,315,4566,4628,4557,4629,4630,4558,2653,315,4542,4559,4631,4551,4552,45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Evaluation Module for Mermaid Diagram Fixing","This module provides the core functionality for evaluating LLM models","on mermaid diagram fixing tasks using multiple MCP servers. It includes:","- Schema definitions for inputs and outputs","- Custom evaluators for multi-MCP tool usage validation","- Agent creation and mermaid diagram fixing functions","- Dataset creation and evaluation utilities","- CSV export functionality","- Robust retry logic for handling transient API failures","This module is designed to be imported by multi-model evaluation scripts.","import csv","import hashlib","import importlib.util","import random","import traceback","from typing import Any, Awaitable, Callable, Dict, Iterable, List, Optional","import logfire","from pydantic import BaseModel, ValidationError","from pydantic_ai import Agent","from pydantic_ai.exceptions import ModelHTTPError, UsageLimitExceeded","from pydantic_ai.mcp import MCPServerStdio","from pydantic_ai.usage import UsageLimits","from pydantic_evals import Case, Dataset","from pydantic_evals.evaluators import Evaluator, EvaluatorContext","from pydantic_evals.reporting import EvaluationReport","from agents_mcp_usage.evaluations.mermaid_evals.mermaid_diagrams import ("," invalid_mermaid_diagram_easy,"," invalid_mermaid_diagram_hard,"," invalid_mermaid_diagram_medium,"," valid_mermaid_diagram,","from agents_mcp_usage.factory.model_factory import ("," create_agent as create_agent_with_model,","from agents_mcp_usage.utils import get_mcp_server_path","# Configure logging to logfire if LOGFIRE_TOKEN is set in environment","logfire.configure("," send_to_logfire=\"if-token-present\", service_name=\"evals-pydantic-multi-mcp\"","logfire.instrument_mcp()","logfire.instrument_pydantic_ai()","# Default model configurations","DEFAULT_MODEL = \"gemini-2.5-pro-preview-06-05\"","# Retry configuration","RETRYABLE_HTTP_STATUS_CODES = {429, 500, 502, 503, 504}","# Request limit configuration for calls to the model","REQUEST_LIMIT = 500","REQUEST_USAGE_COLUMN = \"Requests_Used\"","# Defaults tuned for evaluation runs (avoid hammering endpoints on 429/5xx)","MAX_RETRY_ATTEMPTS = 20","BASE_RETRY_DELAY = 2.0 # seconds","MAX_RETRY_DELAY = 60.0 # seconds","# Dynamically import validate_mermaid_diagram from the mermaid_validator.py file","mermaid_validator_path = str(get_mcp_server_path(\"mermaid_validator.py\"))","spec = importlib.util.spec_from_file_location(\"mermaid_validator\", mermaid_validator_path)","mermaid_validator = importlib.util.module_from_spec(spec)","sys.modules[\"mermaid_validator\"] = mermaid_validator","spec.loader.exec_module(mermaid_validator)","validate_mermaid_diagram = mermaid_validator.validate_mermaid_diagram","# ============================================================================","# Retry Utilities","class MermaidValidatorUnavailableError(RuntimeError):"," \"\"\"Raised when Mermaid validation cannot be performed."," This is treated as *fatal* since it invalidates the evaluation (fail-fast).","def _iter_base_exceptions(exception: BaseException) -> Iterable[BaseException]:"," \"\"\"Yield underlying exceptions, flattening `ExceptionGroup`s.\"\"\""," if isinstance(exception, BaseExceptionGroup):"," for exc in exception.exceptions:"," yield from _iter_base_exceptions(exc)"," yield exception","MERMAID_VALIDATOR_ERROR_HINTS = ("," \"mermaid_validator\","," \"mermaid-validator\","," \"validate_mermaid_diagram\",","def is_mermaid_validator_unavailable_error(exception: BaseException) -> bool:"," \"\"\"Best-effort detection for Mermaid validator MCP/server failures.\"\"\""," if isinstance(exception, MermaidValidatorUnavailableError):"," return True"," text = f\"{type(exception).__name__}: {exception}\".lower()"," return any(hint in text for hint in MERMAID_VALIDATOR_ERROR_HINTS)","def compute_exponential_backoff_delay("," attempt: int,"," base_delay: float = BASE_RETRY_DELAY,"," max_delay: float = MAX_RETRY_DELAY,"," jitter: bool = True,",") -> float:"," \"\"\"Compute exponential backoff delay for an attempt (0-indexed).\"\"\""," delay = min(base_delay * (2**attempt), max_delay)"," if jitter:"," delay = delay * (0.5 + random.random() * 0.5) # 50% jitter"," return delay","def is_retryable_error(exception: Exception) -> bool:"," \"\"\"Checks if an exception is retryable."," This function checks if the given exception is a retryable HTTP error or a"," general connection error."," For `ExceptionGroup`s we inspect contained exceptions and default to"," retryable *unless* the group clearly contains non-retryable signals (usage"," limits, per-case timeouts) or a fatal Mermaid validator failure."," return False"," # Per-case timeouts should not be retried (run-level timeouts are handled separately)"," if isinstance(exception, asyncio.TimeoutError):"," inner_exceptions = list(_iter_base_exceptions(exception))"," # Fail-fast (fatal)"," if any(is_mermaid_validator_unavailable_error(exc) for exc in inner_exceptions):"," return False"," # Non-retryable (policy)"," if any(isinstance(exc, UsageLimitExceeded) for exc in inner_exceptions):"," if any(isinstance(exc, asyncio.TimeoutError) for exc in inner_exceptions):"," # Explicit retryables"," if any("," isinstance(exc, ModelHTTPError)"," and exc.status_code in RETRYABLE_HTTP_STATUS_CODES"," for exc in inner_exceptions"," ):"," return True"," if any(isinstance(exc, (ConnectionError, OSError)) for exc in inner_exceptions):"," # Default for groups: treat as retryable (matches `error_ExceptionGroup` policy)"," if isinstance(exception, ModelHTTPError):"," return exception.status_code in RETRYABLE_HTTP_STATUS_CODES"," # Also retry on general connection errors that might be transient"," if isinstance(exception, (ConnectionError, OSError)):"," return False","async def exponential_backoff_retry("," func_call: Callable[[], Awaitable[Any]],"," max_attempts: int = MAX_RETRY_ATTEMPTS,"," is_retryable: Callable[[Exception], bool] = is_retryable_error,",") -> Any:"," \"\"\"Executes a function with exponential backoff retry logic."," This function attempts to execute the given asynchronous function call,"," retrying with an exponential backoff delay if a retryable error occurs."," func_call: The async function to retry."," max_attempts: The maximum number of retry attempts."," base_delay: The base delay between retries in seconds."," max_delay: The maximum delay between retries in seconds."," jitter: Whether to add random jitter to the delays."," The result of the function call."," Raises:"," The last exception if all retries are exhausted."," last_exception = None"," for attempt in range(max_attempts):"," return await func_call()"," except Exception as e:"," last_exception = e"," if not is_retryable(e):"," logfire.warning("," \"Non-retryable error encountered\","," error_type=type(e).__name__,"," error_message=str(e),"," attempt=attempt + 1,"," )"," raise"," if attempt == max_attempts - 1:"," logfire.error("," \"Max retry attempts exhausted\","," attempts=max_attempts,"," break"," delay = compute_exponential_backoff_delay("," attempt, base_delay=base_delay, max_delay=max_delay, jitter=jitter"," logfire.warning("," \"Retryable error encountered, retrying\","," error_type=type(e).__name__,"," error_message=str(e),"," attempt=attempt + 1,"," max_attempts=max_attempts,"," delay_seconds=delay,"," await asyncio.sleep(delay)"," # Re-raise the last exception if all retries failed"," if last_exception:"," raise last_exception"," # This should never be reached, but just in case"," raise RuntimeError(\"Unexpected error in retry logic\")","# MCP Server Configuration","def get_mcp_servers() -> List[MCPServerStdio]:"," \"\"\"Gets the configured MCP servers for the evaluation."," This function returns a list of MCP servers required for the evaluation,"," including the local example server and the mermaid validator server."," A list of configured MCP servers."," local_server = MCPServerStdio("," command=\"uv\","," args=["," \"run\","," str(get_mcp_server_path(\"example_server.py\")),"," \"stdio\","," mermaid_server = MCPServerStdio("," str(get_mcp_server_path(\"mermaid_validator.py\")),"," return [local_server, mermaid_server]","def create_agent("," model: str = DEFAULT_MODEL, model_settings: Dict[str, Any] = None",") -> Agent:"," \"\"\"Creates an agent with MCP servers for the specified model."," This function initializes and returns an agent with the necessary MCP"," servers and model settings using the new model factory."," model: The model to use for the agent."," model_settings: Optional model-specific settings."," A configured Agent instance."," if model_settings is None:"," model_settings = {}"," # Use the new model factory for all models"," return create_agent_with_model("," model=model,"," mcp_servers=get_mcp_servers(),"," model_settings=model_settings,","# Schema Definitions","class MermaidInput(BaseModel):"," \"\"\"Input schema for mermaid diagram fixing.\"\"\""," invalid_diagram: str"," case_name: Optional[str] = None"," \"\"\"Output schema for mermaid diagram fixing with comprehensive metrics.\"\"\""," fixed_diagram: str"," failure_reason: str = \"\" # Track why a case failed"," metrics: Dict[str, Any] = {} # Capture LLM usage metrics"," tools_used: List[str] = [] # Track which MCP tools were called","# Debug Trace Capture","_SLUG_RE = re.compile(r\"[^a-zA-Z0-9]+\")","def _slugify(value: str) -> str:"," slug = _SLUG_RE.sub(\"_\", value).strip(\"_\")"," return slug or \"unknown\"","def _short_hash(value: str) -> str:"," return hashlib.sha256(value.encode(\"utf-8\")).hexdigest()[:12]","def _usage_limits_to_dict(usage_limits: UsageLimits) -> Dict[str, Any]:"," data: Dict[str, Any] = {}"," for key in ("," \"request_limit\","," \"request_tokens_limit\","," \"response_tokens_limit\","," \"total_tokens_limit\","," ):"," if hasattr(usage_limits, key):"," value = getattr(usage_limits, key)"," if value is not None:"," data[key] = value"," return data","def _usage_to_dict(usage: Any) -> Dict[str, Any]:"," \"\"\"Best-effort conversion of PydanticAI usage objects to primitives for JSON.\"\"\""," details = getattr(usage, \"details\", None) or {}"," \"requests\": getattr(usage, \"requests\", None),"," \"request_tokens\": getattr(usage, \"request_tokens\", None),"," \"response_tokens\": getattr(usage, \"response_tokens\", None),"," \"total_tokens\": getattr(usage, \"total_tokens\", None),"," \"details\": details,","def _exception_to_dict(exception: BaseException) -> Dict[str, Any]:"," data: Dict[str, Any] = {"," \"type\": type(exception).__name__,"," \"message\": str(exception),"," \"traceback\": traceback.format_exception(exception),"," data.update("," \"status_code\": exception.status_code,"," \"model_name\": exception.model_name,"," \"body\": exception.body,"," data[\"exceptions\"] = [_exception_to_dict(e) for e in exception.exceptions]","def _summarize_messages(messages: Any) -> Dict[str, Any]:"," \"\"\"Create small derived fields to quickly spot tool loops / excessive turn usage."," Accepts the parsed output of `all_messages_json()`."," if not isinstance(messages, list):"," summary: Dict[str, Any] = {"," \"message_count\": len(messages),"," \"request_count\": 0,"," \"response_count\": 0,"," \"tool_call_count\": 0,"," \"tool_return_count\": 0,"," \"tool_names_unique\": [],"," \"tool_calls_by_name\": {},"," \"tool_sequence\": [],"," tools_seen_ordered: List[str] = []"," tool_calls_by_name: Dict[str, int] = {}"," for msg in messages:"," if not isinstance(msg, dict):"," kind = msg.get(\"kind\")"," if kind == \"request\":"," summary[\"request_count\"] += 1"," elif kind == \"response\":"," summary[\"response_count\"] += 1"," parts = msg.get(\"parts\") or []"," if not isinstance(parts, list):"," for part in parts:"," if not isinstance(part, dict):"," part_kind = part.get(\"part_kind\")"," tool_name = part.get(\"tool_name\")"," tool_call_id = part.get(\"tool_call_id\")"," if tool_name and tool_name not in tools_seen_ordered:"," tools_seen_ordered.append(tool_name)"," if part_kind in {\"tool-call\", \"builtin-tool-call\"}:"," summary[\"tool_call_count\"] += 1"," if tool_name:"," tool_calls_by_name[tool_name] = tool_calls_by_name.get(tool_name, 0) + 1"," summary[\"tool_sequence\"].append("," {\"event\": \"call\", \"tool_name\": tool_name, \"tool_call_id\": tool_call_id}"," if part_kind in {\"tool-return\", \"builtin-tool-return\"}:"," summary[\"tool_return_count\"] += 1"," {\"event\": \"return\", \"tool_name\": tool_name, \"tool_call_id\": tool_call_id}"," summary[\"tool_names_unique\"] = tools_seen_ordered"," summary[\"tool_calls_by_name\"] = tool_calls_by_name","class MermaidEvalTraceContext:"," \"\"\"Per-case debug trace capture config passed down from evaluation runners.\"\"\""," enabled: bool"," trace_dir: str"," model: str"," planned_run_index: Optional[int] = None # 0-based planned run index"," run_attempt: Optional[int] = None # 1-based attempt counter at runner-level"," evaluation_name: Optional[str] = None","def _build_trace_path(trace_ctx: MermaidEvalTraceContext, inputs: MermaidInput) -> str:"," model_slug = _slugify(trace_ctx.model)"," case_slug = _slugify(inputs.case_name) if inputs.case_name else f\"hash_{_short_hash(inputs.invalid_diagram)}\""," run_part = ("," f\"run{(trace_ctx.planned_run_index + 1):03d}\""," if trace_ctx.planned_run_index is not None"," else \"run000\""," attempt_part = ("," f\"attempt{trace_ctx.run_attempt:03d}\""," if trace_ctx.run_attempt is not None"," else \"attempt000\""," model_dir = os.path.join(trace_ctx.trace_dir, model_slug)"," filename = f\"model={model_slug}__{run_part}__{attempt_part}__case={case_slug}.json\""," return os.path.join(model_dir, filename)","def _write_trace(trace_path: str, trace_record: Dict[str, Any]) -> None:"," \"\"\"Best-effort writer; never raise (debug traces should not break evals).\"\"\""," os.makedirs(os.path.dirname(trace_path), exist_ok=True)"," tmp_path = f\"{trace_path}.tmp\""," with open(tmp_path, \"w\", encoding=\"utf-8\") as f:"," json.dump(trace_record, f, indent=2, ensure_ascii=False, sort_keys=False)"," os.replace(tmp_path, trace_path)"," except Exception as e: # pragma: no cover"," logfire.warning("," \"Failed to write debug trace file\","," trace_path=trace_path,"," error_type=type(e).__name__,"," error=str(e),","# Custom Evaluators","class UsedBothMCPTools(Evaluator[MermaidInput, MermaidOutput]):"," \"\"\"Evaluator to check if both MCP tools were used.\"\"\""," async def evaluate("," self, ctx: EvaluatorContext[MermaidInput, MermaidOutput]"," ) -> float:"," \"\"\"Evaluates if both MCP tools were used in the given context."," This method checks the tools used in the output and returns a score"," based on whether tools from both MCP servers were utilized."," Args:"," ctx: The evaluator context containing the input and output."," Returns:"," A score of 1.0 if both tools were used, 0.5 if one was used,"," and 0.0 otherwise."," if not ctx.output or not ctx.output.tools_used:"," return 0.0"," # Look for tools from both MCP servers"," has_example_server_tool = any("," \"example\" in tool.lower() or \"time\" in tool.lower()"," for tool in ctx.output.tools_used"," has_mermaid_server_tool = any("," \"mermaid\" in tool.lower() or \"validate\" in tool.lower()"," if has_example_server_tool and has_mermaid_server_tool:"," return 1.0"," elif has_example_server_tool or has_mermaid_server_tool:"," return 0.5 # Partial credit for using one server","class UsageLimitNotExceeded(Evaluator[MermaidInput, MermaidOutput]):"," \"\"\"Evaluator to detect usage limit failures.\"\"\""," \"\"\"Checks if the case failed due to usage limits being exceeded."," This method examines the output for a usage limit failure reason and"," returns a score accordingly."," ctx: The evaluator context."," 0.0 if a usage limit failure occurred, 1.0 otherwise."," if ctx.output and ctx.output.failure_reason == \"usage_limit_exceeded\":"," \"Case failed due to usage limit exceeded\","," case_name=getattr(ctx, \"case_name\", \"unknown\"),"," # Return 1.0 if no usage limit failure occurred"," return 1.0","class MermaidDiagramValid(Evaluator[MermaidInput, MermaidOutput]):"," \"\"\"Evaluator to check if the mermaid diagram is valid.\"\"\""," \"\"\"Evaluates if the generated mermaid diagram is valid."," This method validates the mermaid diagram in the output, handling"," retries and logging the results."," 1.0 if the diagram is valid, 0.0 otherwise."," # Skip validation if there was a failure"," if ctx.output and ctx.output.failure_reason:"," logfire.info("," \"Skipping diagram validation due to failure\","," failure_reason=ctx.output.failure_reason,"," # Strip whitespace, remove backticks and ```mermaid markers"," input_str = ctx.output.fixed_diagram.strip()"," # Remove ```mermaid and ``` markers"," if input_str.startswith(\"```mermaid\"):"," input_str = input_str[len(\"```mermaid\") :].strip()"," if input_str.endswith(\"```\"):"," input_str = input_str[:-3].strip()"," # Remove any remaining backticks"," input_str = input_str.replace(\"`\", \"\")"," logfire.info("," \"Evaluating mermaid diagram validity\","," diagram_length=len(input_str),"," diagram_preview=input_str[:100],"," # Use the MCP server's validation function with retry logic"," result = await exponential_backoff_retry("," lambda: validate_mermaid_diagram(input_str)"," except MermaidValidatorUnavailableError:"," raise"," logfire.error("," \"Failed to validate mermaid diagram after retries\","," raise MermaidValidatorUnavailableError("," \"Mermaid validator MCP server unavailable\""," ) from e"," if ("," not result.is_valid"," and result.error_message"," and result.error_message.startswith(\"Error validating mermaid diagram:\")"," # If the validator itself is failing, results are invalid → fail-fast."," raise MermaidValidatorUnavailableError(result.error_message)"," if result.is_valid:"," logfire.info(\"Mermaid diagram validation succeeded\")"," \"Mermaid diagram validation failed\", error_message=result.error_message"," return 1.0 if result.is_valid else 0.0","# Core Evaluation Functions","async def fix_mermaid_diagram("," inputs: MermaidInput,"," model: str = DEFAULT_MODEL,"," *,"," trace_ctx: MermaidEvalTraceContext | None = None,",") -> MermaidOutput:"," \"\"\"Fixes an invalid mermaid diagram using an agent with multiple MCP servers."," In normal mode, we return aggregate usage metrics and tool names."," In debug trace mode, we additionally write a per-case JSON file containing:"," input, prompt, full message history (incl. tool call args + tool return payloads),"," and usage."," inputs: The input containing the invalid diagram."," trace_ctx: Optional debug trace config (used by multi-model runner)."," A MermaidOutput object with the fixed diagram and captured metrics."," query = ("," \"Add the current time and fix the mermaid diagram syntax using the validator: \""," f\"{inputs.invalid_diagram}. Return only the fixed mermaid diagram between backticks.\""," trace_enabled = bool(trace_ctx and trace_ctx.enabled)"," # Create a fresh agent for each invocation to avoid concurrent usage issues"," current_agent = create_agent(model)"," # NOTE: request_limit is the most common limiter we hit in tool-loop failure modes."," usage_limits = UsageLimits(request_limit=REQUEST_LIMIT)"," captured_messages_json: str | None = None"," captured_usage: Any | None = None"," captured_agent_run_id: str | None = None"," def _maybe_write_trace("," *,"," status: str,"," failure_reason: str,"," output_text: str | None,"," extracted_diagram: str | None,"," exception: BaseException | None = None,"," ) -> None:"," nonlocal captured_messages_json, captured_usage, captured_agent_run_id"," if not trace_enabled or trace_ctx is None:"," return"," trace_path = _build_trace_path(trace_ctx, inputs)"," messages_parsed: Any = None"," if captured_messages_json:"," try:"," messages_parsed = json.loads(captured_messages_json)"," except Exception:"," messages_parsed = captured_messages_json"," trace_record: Dict[str, Any] = {"," \"schema_version\": 1,"," \"created_at\": datetime.now().isoformat(),"," \"status\": status,"," \"model\": model,"," \"runner\": {"," \"evaluation_name\": trace_ctx.evaluation_name,"," \"planned_run_index\": trace_ctx.planned_run_index,"," \"planned_run_number\": ("," trace_ctx.planned_run_index + 1"," if trace_ctx.planned_run_index is not None"," else None"," ),"," \"run_attempt\": trace_ctx.run_attempt,"," },"," \"case\": {"," \"case_name\": inputs.case_name,"," \"invalid_diagram\": inputs.invalid_diagram,"," \"invalid_diagram_len\": len(inputs.invalid_diagram),"," \"invalid_diagram_sha256\": hashlib.sha256("," inputs.invalid_diagram.encode(\"utf-8\")"," ).hexdigest(),"," \"prompt\": {\"query\": query},"," \"usage_limits\": _usage_limits_to_dict(usage_limits),"," \"agent\": {"," \"run_id\": captured_agent_run_id,"," \"all_messages\": messages_parsed,"," \"messages_summary\": _summarize_messages(messages_parsed),"," \"result\": {"," \"output_text\": output_text,"," \"extracted_diagram\": extracted_diagram,"," \"failure_reason\": failure_reason,"," \"usage\": _usage_to_dict(captured_usage) if captured_usage is not None else {},"," if exception is not None:"," trace_record[\"error\"] = _exception_to_dict(exception)"," _write_trace(trace_path, trace_record)"," async def _run_agent():"," async with current_agent.run_mcp_servers():"," if trace_enabled:"," agent_run = None"," async with current_agent.iter("," query, usage_limits=usage_limits, infer_name=False"," ) as _agent_run:"," agent_run = _agent_run"," async for _node in agent_run:"," # We don't persist nodes (they're not stable/JSON-serializable),"," # all useful debug info is in `all_messages_json()`."," pass"," # NOTE: this may raise if the run failed during finalisation;"," # our `finally` block below will still capture partial messages/usage."," return agent_run.result"," finally:"," if agent_run is not None:"," captured_agent_run_id = agent_run.run_id"," try:"," captured_messages_json = agent_run.all_messages_json()"," except Exception:"," captured_messages_json = None"," captured_usage = agent_run.usage()"," captured_usage = None"," # Non-trace mode: keep the existing behaviour."," result = await current_agent.run(query, usage_limits=usage_limits)"," return result"," # Use retry logic for the agent run"," result = await exponential_backoff_retry(_run_agent)"," # Extract usage metrics"," usage = captured_usage or result.usage()"," metrics = _usage_to_dict(usage)"," # Extract tool usage information from agent messages"," tools_used: List[str] = []"," for message in result.all_messages():"," for part in message.parts:"," tool_name = getattr(part, \"tool_name\", None)"," tools_used.append(tool_name)"," tools_used = list(dict.fromkeys(tools_used)) # unique, preserve order"," output = result.output"," # Extract the diagram from between backticks"," if \"```\" in output:"," start = output.find(\"```\")"," end = output.rfind(\"```\") + 3"," diagram = output[start:end]"," diagram = output"," _maybe_write_trace("," status=\"success\","," failure_reason=\"\","," output_text=output,"," extracted_diagram=diagram,"," return MermaidOutput(fixed_diagram=diagram, metrics=metrics, tools_used=tools_used)"," except MermaidValidatorUnavailableError as e:"," status=\"error\","," failure_reason=\"mermaid_validator_unavailable\","," output_text=None,"," extracted_diagram=None,"," exception=e,"," raise"," except UsageLimitExceeded as e:"," \"Usage limit exceeded during mermaid diagram fix\","," error_message=str(e),"," model=model,"," status=\"failure\","," failure_reason=\"usage_limit_exceeded\","," return MermaidOutput("," fixed_diagram=\"\","," metrics={},"," tools_used=[],"," except ModelHTTPError as e:"," logfire.error("," \"HTTP error during mermaid diagram fix after retries\","," status_code=e.status_code,"," model_name=e.model_name,"," failure_reason=f\"http_error_{e.status_code}\","," except ValidationError as e:"," \"Response validation error during mermaid diagram fix\","," failure_reason=\"response_validation_failed\","," except asyncio.TimeoutError as e:"," \"Timeout error during mermaid diagram fix\","," failure_reason=\"agent_timeout\","," # Mermaid validator MCP failures are fatal for evaluations (fail-fast)"," if is_mermaid_validator_unavailable_error(e):"," wrapped = MermaidValidatorUnavailableError("," _maybe_write_trace("," status=\"error\","," failure_reason=\"mermaid_validator_unavailable\","," output_text=None,"," extracted_diagram=None,"," exception=wrapped,"," raise wrapped from e"," # Provide more specific error categorization"," error_type = type(e).__name__"," failure_reason = \"\""," if isinstance(e, BaseExceptionGroup):"," inner_exceptions = list(_iter_base_exceptions(e))"," if any("," is_mermaid_validator_unavailable_error(exc) for exc in inner_exceptions"," ):"," raise MermaidValidatorUnavailableError("," \"Mermaid validator MCP server unavailable\""," ) from e"," if any(isinstance(exc, UsageLimitExceeded) for exc in inner_exceptions):"," failure_reason = \"usage_limit_exceeded\""," elif any(isinstance(exc, asyncio.TimeoutError) for exc in inner_exceptions):"," # Policy: per-case timeouts should not be retried"," failure_reason = \"agent_timeout\""," http_exc = next("," ("," exc"," for exc in inner_exceptions"," if isinstance(exc, ModelHTTPError)"," ),"," None,"," if http_exc:"," failure_reason = f\"http_error_{http_exc.status_code}\""," elif any("," isinstance(exc, (ConnectionError, OSError))"," for exc in inner_exceptions"," ):"," failure_reason = \"connection_error\""," else:"," # Default: ambiguous ExceptionGroup (treated as retryable at run-level)"," failure_reason = \"error_ExceptionGroup\""," error_lower = str(e).lower()"," if \"timeout\" in error_lower or \"timed out\" in error_lower:"," failure_reason = \"timeout_error\""," elif \"connection\" in error_lower or \"network\" in error_lower:"," failure_reason = \"connection_error\""," elif \"rate limit\" in error_lower or \"quota\" in error_lower:"," failure_reason = \"rate_limit_error\""," failure_reason = f\"error_{error_type}\""," \"Unexpected error during mermaid diagram fix after retries\","," error_type=error_type,"," categorized_failure_reason=failure_reason,"," failure_reason=failure_reason,"," # Return empty diagram with failure reason to indicate general failure","def create_evaluation_dataset("," judge_model: str = DEFAULT_MODEL,",") -> Dataset[MermaidInput, MermaidOutput, Any]:"," \"\"\"Creates the dataset for evaluating mermaid diagram fixing."," This function constructs a dataset with test cases of varying difficulty"," and a set of evaluators for judging the results."," judge_model: The model to use for LLM judging."," The evaluation dataset."," return Dataset[MermaidInput, MermaidOutput, Any]("," # Construct 3 tests, each asks the LLM to fix an invalid mermaid diagram of increasing difficulty"," cases=["," Case("," name=\"fix_invalid_diagram_easy\","," inputs=MermaidInput("," invalid_diagram=invalid_mermaid_diagram_easy,"," case_name=\"fix_invalid_diagram_easy\","," expected_output=MermaidOutput("," fixed_diagram=valid_mermaid_diagram,"," failure_reason=\"\","," metrics={},"," tools_used=[],"," metadata={\"test_type\": \"mermaid_easy_fix\"},"," ),"," name=\"fix_invalid_diagram_medium\","," invalid_diagram=invalid_mermaid_diagram_medium,"," case_name=\"fix_invalid_diagram_medium\","," metadata={\"test_type\": \"mermaid_medium_fix\"},"," name=\"fix_invalid_diagram_hard\","," invalid_diagram=invalid_mermaid_diagram_hard,"," case_name=\"fix_invalid_diagram_hard\","," metadata={\"test_type\": \"mermaid_hard_fix\"},"," evaluators=["," UsedBothMCPTools(),"," UsageLimitNotExceeded(),"," MermaidDiagramValid(),"," # LLMJudge("," # rubric=\"The response only contains a mermaid diagram inside the fixed_diagram field, no other text. Ignore the metrics, failure_reason, and tools_used fields.\","," # include_input=False,"," # model=judge_model,"," # ),"," # rubric=\"The fixed_diagram field should maintain the same overall structure and intent as the expected output diagram while fixing any syntax errors. Check if nodes, connections, and labels are preserved. The current time placeholder should be replaced with a valid datetime. Ignore the metrics, failure_reason, and tools_used fields.\",","# Utility Functions","def get_timestamp_prefix() -> str:"," \"\"\"Gets a timestamp prefix in the format yyyy-mm-dd_H-M-s."," A string representing the current timestamp."," now = datetime.now()"," return now.strftime(\"%Y-%m-%d_%H-%M-%S\")","def write_mermaid_results_to_csv("," report: EvaluationReport, model: str, output_dir: str = \"./mermaid_eval_results\"",") -> str:"," \"\"\"Writes mermaid evaluation results with metrics to a CSV file."," This function takes an evaluation report and writes the results to a CSV"," file, including scores and metrics."," report: The evaluation report from pydantic_evals."," model: The model name used for evaluation."," output_dir: The directory to write the CSV file to."," The path to the created CSV file."," os.makedirs(output_dir, exist_ok=True)"," timestamp = get_timestamp_prefix()"," filepath = os.path.join("," output_dir, f\"{timestamp}_mermaid_results_{model.replace(':', '_')}.csv\""," # Collect all unique evaluator and metric names"," all_evaluator_names = set()"," all_metric_names = set()"," for case in report.cases:"," all_evaluator_names.update(case.scores.keys())"," if hasattr(case.output, \"metrics\") and case.output.metrics:"," all_metric_names.update(case.output.metrics.keys())"," # Build CSV headers"," headers = ["," \"Fixed_Diagram_Length\","," \"Failure_Reason\","," \"Tools_Used\","," for evaluator in sorted(all_evaluator_names):"," headers.append(f\"Score_{evaluator}\")"," for metric in sorted(all_metric_names):"," headers.append(f\"Metric_{metric}\")"," # Track how many model invocations were used (e.g., \"87/500\")"," headers.append(REQUEST_USAGE_COLUMN)"," # Write the CSV file"," with open(filepath, \"w\", newline=\"\", encoding=\"utf-8\") as csvfile:"," writer = csv.writer(csvfile)"," writer.writerow(headers)"," for case in report.cases:"," row = ["," model,"," case.name,"," case.task_duration,"," len(case.output.fixed_diagram)"," if case.output and case.output.fixed_diagram"," else 0,"," case.output.failure_reason if case.output else \"\","," \"|\".join(case.output.tools_used)"," if case.output and case.output.tools_used"," else \"\","," ]"," # Add evaluator scores"," for evaluator in sorted(all_evaluator_names):"," if evaluator in case.scores:"," row.append(case.scores[evaluator].value)"," row.append(\"\")"," # Add metrics"," for metric in sorted(all_metric_names):"," if ("," case.output"," and hasattr(case.output, \"metrics\")"," and case.output.metrics"," and metric in case.output.metrics"," metric_value = case.output.metrics[metric]"," if isinstance(metric_value, dict):"," row.append(str(metric_value))"," else:"," row.append(metric_value)"," # Add request-usage summary as the last column"," requests_used = None"," if ("," case.output"," and hasattr(case.output, \"metrics\")"," and isinstance(case.output.metrics, dict)"," requests_used = case.output.metrics.get(\"requests\")"," row.append(requests_used if requests_used is not None else \"na\")"," writer.writerow(row)"," print(f\"Mermaid evaluation results written to {filepath}\")"," return filepath","# Single Model Evaluation Function","async def run_evaluations("," export_csv: bool = True,"," output_dir: str = \"./mermaid_eval_results\",",") -> EvaluationReport:"," \"\"\"Runs the evaluations on the mermaid diagram fixing task."," This function sets up the evaluation dataset, runs the evaluation for a"," given model, and exports the results to a CSV file."," export_csv: Whether to export the results to a CSV file."," output_dir: The directory to save the results to."," The evaluation report."," dataset = create_evaluation_dataset(judge_model)"," # Create a wrapper that includes the model parameter"," async def fix_with_model(inputs: MermaidInput) -> MermaidOutput:"," return await fix_mermaid_diagram(inputs, model=model)"," report = await dataset.evaluate("," fix_with_model,"," name=f\"{model}-multi-mcp-mermaid-diagram-fix-evals\","," max_concurrency=1, # Run one evaluation at a time"," report.print(include_input=False, include_output=False)"," if export_csv:"," csv_path = write_mermaid_results_to_csv(report, model, output_dir)"," print(f\"Results exported to: {csv_path}\")"," return report","# Main Execution (for standalone use)"," # You can use different models for the agent and the judge"," # agent_model = os.getenv(\"AGENT_MODEL\", DEFAULT_MODEL)"," # agent_model = \"gemini-2.5-pro-preview-06-05\""," # agent_model = \"openai:o4-mini\""," agent_model = \"gemini-2.5-flash\""," judge_model = os.getenv(\"JUDGE_MODEL\", DEFAULT_MODEL)"," async def run_all():"," await run_evaluations("," model=agent_model, judge_model=judge_model, export_csv=True"," asyncio.run(run_all())",[4793],{"_2587":990,"_2588":4794,"_673":4795,"_2591":57},275,[4796,315,4797,315,4798,315,4799,315,4800,315,4801,4802,4803,4804,4805,4806,4807,4805,4808,4809,4805,4810,4811,4805,4812,4813,4814,315,4815,315,4816,315,3669,3773,4817,315,4818,4819,4820,4821,4822,4823,3057,4824,4825,4826,4827,4828,315,4829,4830,4831,4832,4833,4834,4828,315,4835,4836,4837,4838,4828,2963,4839,2963,4840,4841,4842,4843,2963,4844,4845,4846,2963,4847,4848,4849,4850,2963,4851,4852,4853,4854,315,4855,4856,4857,4858,4859,4860,4861,4862,315,4863,4864,4865,4866,4867,4868,4869,4870,3689,315,4871,315,4872,315,3669,4873,4874,4875,4876,4877,4878,4879,2963,4880,2963,4881,2963,4882,4883,2963,4884,4885,4886,2963,4887,2963,4888,4889,2963,4890,4891,4892,4893,4894,4895,4896,4828,2963,4897,4898,2963,4899,4900,2963,4901,4902,4903,2963,4904,3689,315,4905,315,4906,315,4907,315,4908,315,3705,4909,3689,315,4910,4911,4912,4913,4914,4915,4916,315,4917,3705,4918,3689,315,4919,315,4920,315,4921,315,3705,4922,3689,315,4910,4923,4924,4925,4926,315,4927,315,4928,315,4929,315,3705,4930,3689,315,4910,4931,4932,4933,4934,315,4935,315,4936,315,4937,315,3705,4938,3689,315,4910,4939,4940,4941,4942,315,4943,315,4944,315,4945,315,3705,4946,4947,315,4948,4949,315,4950,4951,315,4952,4953,3689,315,4910,4954,4955,4956,4957,4958,315,315,4959,315,4960,315,4961,4962,4963,4964,315,4965,315,4966,315,4967,315,4968,4969,4970,315,4971],"# Basic MCP Usage","This directory contains examples of using tools from a single Model Context Protocol (MCP) with various LLM agent frameworks.","Each script demonstrates how to connect to a single local MCP server and use it with a different agent framework. The examples follow a consistent pattern while highlighting framework-specific approaches to MCP server interaction.","## Quickstart","1. Configure `.env` and API keys following instructions in the [README.md](README.md)","2. Run any example script:"," ```bash"," # Run the Pydantic-AI example"," uv run agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp.py"," "," # Run the OpenAI Agents example"," uv run agents_mcp_usage/basic_mcp/basic_mcp_use/oai-agent_mcp.py"," # Run the LangGraph example"," uv run agents_mcp_usage/basic_mcp/basic_mcp_use/langgraph_mcp.py"," # Run the Google ADK example"," uv run agents_mcp_usage/basic_mcp/basic_mcp_use/adk_mcp.py"," ## Launch ADK web UI for visual interaction"," make adk_basic_ui"," ```","4. Check the console output or Logfire for results.","### Basic MCP Architecture"," User((User)) --> |\"Run script
(e.g., pydantic_mcp.py)\"| Agent"," subgraph \"Agent Frameworks\""," Agent[Agent]"," ADK[\"Google ADK
(adk_mcp.py)\"]"," LG[\"LangGraph
(langgraph_mcp.py)\"]"," OAI[\"OpenAI Agents
(oai-agent_mcp.py)\"]"," PYD[\"Pydantic-AI
(pydantic_mcp.py)\"]"," Agent --> ADK"," Agent --> LG"," Agent --> OAI"," Agent --> PYD"," end"," subgraph \"Python MCP Server\""," MCP[\"Model Context Protocol Server
(mcp_servers/example_server.py)\"]"," Tools[\"Tools
- add(a, b)
- get_current_time()\"]"," Resources[\"Resources
- greeting://{name}\"]"," MCP --- Tools"," MCP --- Resources"," subgraph \"LLM Providers\""," OAI_LLM[\"OpenAI Models\"]"," GEM[\"Google Gemini Models\"]"," OTHER[\"Other LLM Providers...\"]"," Logfire[(\"Logfire
Tracing\")]"," ADK --> MCP"," LG --> MCP"," OAI --> MCP"," PYD --> MCP"," MCP --> OAI_LLM"," MCP --> GEM"," MCP --> OTHER"," ADK --> Logfire"," LG --> Logfire"," OAI --> Logfire"," PYD --> Logfire"," LLM_Response[(\"Response\")] --> User"," OAI_LLM --> LLM_Response"," GEM --> LLM_Response"," OTHER --> LLM_Response"," %% Node styling for better dark/light mode readability"," classDef userNode fill:#b3e0ff,stroke:#0066cc,stroke-width:2px,color:#000000;"," classDef agentNode fill:#d1c4e9,stroke:#673ab7,stroke-width:1px,color:#000000;"," classDef mcpNode fill:#ffccbc,stroke:#ff5722,stroke-width:2px,color:#000000;"," classDef toolNode fill:#ffe0b2,stroke:#ff9800,stroke-width:1px,color:#000000;"," classDef llmNode fill:#c8e6c9,stroke:#4caf50,stroke-width:1px,color:#000000;"," classDef outputNode fill:#ffcdd2,stroke:#e53935,stroke-width:2px,color:#000000;"," classDef logNode fill:#e1bee7,stroke:#8e24aa,stroke-width:2px,color:#000000;"," %% Apply styles to nodes"," class User,LLM_Response userNode;"," class Agent,ADK,LG,OAI,PYD agentNode;"," class MCP mcpNode;"," class Tools,Resources toolNode;"," class OAI_LLM,GEM,OTHER llmNode;"," class LLM_Response outputNode;"," class Logfire logNode;","The diagram illustrates how MCP serves as a standardised interface between different agent frameworks and LLM providers.The flow shows how users interact with the system by running a specific agent script, which then leverages MCP to communicate with LLM providers, while Logfire provides tracing and observability.","### Basic MCP Sequence Flow","sequenceDiagram"," participant User"," participant Agent as Agent Framework"," participant MCP as Python MCP Server"," participant LLM as LLM Provider"," participant Tools as MCP Tools"," participant Logfire as Logfire Tracing"," Note over User,Logfire: Basic MCP Interaction Flow"," User->>Agent: Run agent script with query"," activate Agent"," Agent->>Logfire: Start tracing"," Agent->>MCP: Initialise connection"," activate MCP"," MCP-->>Agent: Connection established"," Agent->>MCP: Send user query"," MCP->>LLM: Generate response/action"," activate LLM"," loop Tool Use Cycle"," LLM-->>MCP: Request tool execution"," MCP->>Tools: Execute tool (e.g., add, get_time)"," activate Tools"," Tools-->>MCP: Return tool result"," deactivate Tools"," MCP->>LLM: Continue with tool result"," LLM-->>MCP: Final response"," deactivate LLM"," MCP-->>Agent: Return response"," deactivate MCP"," Agent->>Logfire: Log completion"," Agent->>User: Display final answer"," deactivate Agent"," Note over User,Logfire: End of interaction","The sequence diagram illustrates the temporal flow of interactions between the user, agent framework, MCP server, LLM provider, and tools. It highlights how the tool execution cycle operates within the MCP architecture.","### Google Agent Development Kit (ADK)","**File:** `adk_mcp.py`","This example demonstrates how to use MCP with Google's Agent Development Kit (ADK 1.3.0).","uv run agents_mcp_usage/basic_mcp/basic_mcp_use/adk_mcp.py","Key features:","- Uses `MCPToolset` with `StdioConnectionParams` for MCP server connection","- Configures a Gemini model using ADK's `LlmAgent`","- Sets up session handling and runner for agent execution","- Implements proper resource management for MCP connections","- Supports ADK web UI through module exports","- Includes Logfire instrumentation for tracing","To run with the ADK web UI:","make adk_basic_ui","### LangGraph","**File:** `langgraph_mcp.py`","This example demonstrates how to use MCP with LangGraph agents.","uv run agents_mcp_usage/basic_mcp/basic_mcp_use/langgraph_mcp.py","- Uses LangChain MCP adapters to load tools","- Creates a ReAct agent with LangGraph","- Demonstrates stdio-based client connection to MCP server","- Uses Gemini model for agent reasoning","### OpenAI Agents","**File:** `oai-agent_mcp.py`","This example demonstrates how to use MCP with OpenAI's Agents package.","uv run agents_mcp_usage/basic_mcp/basic_mcp_use/oai-agent_mcp.py","- Uses OpenAI's Agent and Runner classes","- Connects to MCP server through MCPServerStdio","- Uses OpenAI's o4-mini model","- Includes Logfire instrumentation for both MCP and OpenAI Agents","### Pydantic-AI","**File:** `pydantic_mcp.py`","This example demonstrates how to use MCP with the Pydantic-AI agent framework.","uv run agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp.py","- Uses the simplified Pydantic-AI Agent interface","- Configures MCPServerStdio for MCP communication","- Employs context manager for server lifecycle management","- Includes comprehensive instrumentation for both MCP and Pydantic-AI","### Pydantic-AI with Model Factory Pattern","**File:** `pydantic_mcp_factory.py`","This example demonstrates how to use the model factory pattern to create agents with different providers while maintaining the same MCP functionality.","# Use default Gemini model","uv run agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py","# Use DeepSeek model","uv run agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py --model deepseek","# Use OpenAI model with explicit provider","uv run agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py --model \"openai:gpt-4\"","# List available model aliases","uv run agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py --list-models","- Demonstrates the model factory pattern for multi-provider support","- Supports 13+ providers including Google, OpenAI, Anthropic, AWS Bedrock, DeepSeek, OpenRouter, GitHub, Grok, Perplexity, and local Ollama models","- Command-line interface for easy model switching","- Shows how to handle provider-specific configurations","- Maintains the same MCP server functionality across all providers","## Understanding the Examples","Each example follows a similar pattern:","1. **Environment Setup**: Loading environment variables and configuring logging","2. **Server Connection**: Establishing a connection to the local MCP server","3. **Agent Configuration**: Setting up an agent with the appropriate model","4. **Execution**: Running the agent with a query and handling the response","The examples are designed to be as similar as possible, allowing you to compare how different frameworks approach MCP integration.","## MCP Server","All examples connect to the same MCP server defined in `mcp_servers/example_server.py`. This server provides:","- An addition tool (`add(a, b)`)","- A time tool (`get_current_time()`) ","- A dynamic greeting resource (`greeting://{name}`)","You can modify the MCP server to add your own tools and resources for experimentation.",[4973],{"_2587":990,"_2588":4974,"_673":4975,"_2591":57},216,[3251,4976,315,4977,4978,315,4979,4980,4981,4982,3251,315,3260,2929,2597,315,4026,2599,4030,315,4042,4983,315,2601,315,4043,4984,4046,4047,315,4985,4986,4987,4988,4989,4990,4991,2963,4992,4993,4994,4995,2963,4996,4997,4998,2963,4999,5000,2963,5001,5002,5003,2963,5004,5005,5006,2923,315,315,5007,5008,5009,4185,4186,4187,4188,4189,2778,2608,315,315,5010,5011,5012,5013,5014,5015,315,5016,5017,315,2617,5018,5019,5020,2621,5021,5022,2963,5023,5024,5025,4206,5026,5027,2608,2963,5028,2625,5029,5030,5031,5032,3057,5033,5034,5035,5036,5037,2963,2664,5038,5039,5040,5041,5042,5043,5044,315,315,5045,5046,3568,5047,5048,5049,5050,5051,5052,5053,5054,5055,5053,5056,5057,5053,5058,5059,5053,5060,5061,2689,2608,2963,3570,5062,5063,5064,2608,2963,3570,5065,5066,5067,2608,2963,3570,5068,3593,5069,2608,2963,5070,3570,5071,5072,2608,2963,3570,5073,5074,2608,2963,3570,5075,5076,5077,2608,2963,5078,315,315,2691,5079,2963,5080,5081,5082,5083,5084,5085,5086,5087,5088,5089,5090,2963,5091,5092,2963,5093,5094,5095,5096,5097,5098,5099,5100,2963,5101,5102,4206,5103,5104,5105],"Pydantic MCP Example using Model Factory Pattern","This example demonstrates how to use the model factory to create agents","with different providers while maintaining the same MCP server setup.","Supported model formats:","- Standard: \"gemini-2.5-pro\", \"gpt-4\", \"claude-3-opus\"","- Provider-prefixed: \"openai:gpt-4\", \"deepseek:deepseek-chat\", \"bedrock:us.amazon.nova-pro-v1:0\"","- Local models: \"ollama:llama3.2\", \"ollama:qwen2.5-coder:7b\"","from agents_mcp_usage.factory.model_factory import create_agent","logfire.configure(send_to_logfire=\"if-token-present\", service_name=\"pydantic-basic-mcp-factory\")","# Preset model aliases demonstrating different provider formats","MODEL_ALIASES = {"," # Standard providers (auto-detected)"," \"gemini\": \"gemini-2.5-pro-preview-06-05\","," \"gemini-flash\": \"gemini-2.5-flash\","," \"gpt4\": \"gpt-4\","," \"claude\": \"claude-3-opus-20240229\","," # Explicit provider prefix"," \"openai-o4\": \"openai:o4-mini\","," \"deepseek\": \"deepseek:deepseek-chat\","," \"deepseek-reasoner\": \"deepseek:deepseek-reasoner\","," # AWS Bedrock models"," \"bedrock-nova\": \"bedrock:us.amazon.nova-pro-v1:0\","," \"bedrock-claude\": \"bedrock:us.anthropic.claude-3-5-sonnet-20240620-v1:0\","," # GitHub Models (free tier)"," \"github-grok\": \"github:xai/grok-3-mini\","," # Local models via Ollama"," \"ollama-llama\": \"ollama:llama3.2\","," \"ollama-qwen\": \"ollama:qwen2.5-coder:7b\","," # Other providers"," \"perplexity\": \"perplexity:sonar-pro\","," \"grok\": \"grok:grok-2-1212\",","def get_mcp_server() -> MCPServerStdio:"," \"\"\"Get the MCP server configuration.\"\"\""," return MCPServerStdio(","async def main("," model: str = \"gemini-2.5-pro-preview-06-05\","," query: str = \"Greet Andrew and give him the current time\","," provider_kwargs: Optional[dict] = None",") -> None:"," \"\"\"Runs the Pydantic agent with a given query using the model factory."," This function demonstrates how to use the model factory to create agents"," with different providers while maintaining the same MCP functionality."," model: The model identifier (e.g., \"gemini-2.5-pro\", \"deepseek:deepseek-chat\")"," query: The query to run the agent with"," provider_kwargs: Optional provider-specific configuration (e.g., for Azure)"," # Create MCP server"," server = get_mcp_server()"," # Create agent using the factory"," print(f\"Creating agent with model: {model}\")"," agent = create_agent("," mcp_servers=[server],"," provider_kwargs=provider_kwargs"," # Run the agent"," async with agent.run_mcp_servers():"," result = await agent.run(query)"," print(f\"\\nResult from {model}:\")"," print(result.output)"," # Print usage information if available"," usage = result.usage()"," if usage:"," print(f\"\\nToken usage: {usage.total_tokens} total \""," f\"({usage.request_tokens} in, {usage.response_tokens} out)\")"," print(f\"Error running agent: {e}\")"," print(\"\\nMake sure you have the required API keys set:\")"," print(\"- GEMINI_API_KEY for Gemini models\")"," print(\"- OPENAI_API_KEY for OpenAI models\")"," print(\"- DEEPSEEK_API_KEY for DeepSeek models\")"," print(\"- AWS credentials for Bedrock models\")"," print(\"- Other provider-specific keys as needed\")","def parse_arguments():"," \"\"\"Parse command-line arguments.\"\"\""," description=\"Run Pydantic MCP agent with different model providers\","," formatter_class=argparse.RawDescriptionHelpFormatter,"," epilog=\"\"\"","Examples:"," # Use default Gemini model"," python pydantic_mcp_factory.py"," "," # Use a specific model by alias"," python pydantic_mcp_factory.py --model deepseek"," # Use a model by full name"," python pydantic_mcp_factory.py --model \"openai:gpt-4\""," # Use a custom query"," python pydantic_mcp_factory.py --query \"What's the weather like?\""," # List available model aliases"," python pydantic_mcp_factory.py --list-models"," \"--model\", \"-m\","," default=\"gemini\","," help=\"Model to use (alias or full model string)\""," \"--query\", \"-q\","," default=\"Greet Andrew and give him the current time\","," help=\"Query to send to the agent\""," \"--list-models\", \"-l\","," help=\"List available model aliases and exit\""," # Provider-specific options"," \"--azure-endpoint\","," help=\"Azure endpoint for Azure AI Foundry models\""," \"--azure-api-version\","," help=\"Azure API version for Azure AI Foundry models\""," \"--ollama-base-url\","," default=\"http://localhost:11434/v1\","," help=\"Base URL for Ollama server\""," return parser.parse_args()"," args = parse_arguments()"," # List models and exit if requested"," if args.list_models:"," print(\"Available model aliases:\")"," print(\"-\" * 50)"," for alias, model_string in MODEL_ALIASES.items():"," print(f\"{alias:<20} -> {model_string}\")"," print(\"\\nYou can also use any model string directly, e.g.:\")"," print(\" openrouter:google/gemini-2.5-pro-preview\")"," print(\" fireworks:accounts/fireworks/models/qwq-32b\")"," print(\" together:meta-llama/Llama-3.3-70B-Instruct-Turbo-Free\")"," exit(0)"," # Resolve model alias if applicable"," model = MODEL_ALIASES.get(args.model, args.model)"," # Build provider kwargs if Azure options are provided"," provider_kwargs = {}"," if args.azure_endpoint:"," provider_kwargs[\"azure_endpoint\"] = args.azure_endpoint"," if args.azure_api_version:"," provider_kwargs[\"api_version\"] = args.azure_api_version"," if args.ollama_base_url and model.startswith(\"ollama:\"):"," provider_kwargs[\"base_url\"] = args.ollama_base_url"," # Run the main function"," asyncio.run(main("," query=args.query,"," provider_kwargs=provider_kwargs if provider_kwargs else None"," ))",[5107],{"_2587":990,"_2588":5108,"_673":5109,"_2591":57},255,[5110,315,5111,315,5112,315,5113,315,5114,5115,5116,5117,315,5118,315,5119,315,3782,4983,315,5120,5121,5122,5123,5124,2942,315,5125,5121,5126,5127,2942,3689,315,5128,315,5129,315,5130,5131,5132,5133,315,5134,5135,5136,5137,315,5138,5139,5140,315,5141,315,5142,5143,5144,5145,5146,315,5147,5148,315,5149,5150,5151,5152,5153,5154,315,5155,315,5156,315,5157,5158,5159,315,5160,315,5161,315,5162,315,3705,5163,5164,3689,315,5165,315,3782,5121,5166,5167,5168,5169,5170,2746,2942,3689,315,5171,315,5172,5173,5174,5175,5176,315,5177,315,5149,5150,5178,5179,5180,5181,315,5182,315,5183,315,3782,5184,5185,5186,2963,5187,5188,2942,3689,315,5189,5190,5191,315,5192,315,5193,315,3782,5194,5195,5196,5197,5198,5199,5200,5201,2733,2690,2923,3689,315,3668,315,5202,315,5203,5204,5205,5206,315,5207,315,5208,5209,5210,5211,5212,315,5213,315,5214,5215,5216,5217,5218,315,5219,315,3782,4983,315,5220,5221,5222,5223,5224,5225,315,5226,5025,4206,5227,2608,5228,5229,3689,315,5230,315,5231,315,3705,5232,5233,5234,5235,315,5236,5237,5238,5239,5240,5241,5242,5243,315,5244,5245,5246,3689,315,5247,315,5248,3782,5249,5250,5251,2963,5252,5253,5254,5255,5256,5257,2653,2608,5258,3689,315,5259,3782,4983,315,5121,5260,5127,2942,3689,315,5261],"# Model Factory Documentation","The model factory pattern provides a centralized way to create models across different providers in PydanticAI, leveraging the framework's built-in provider support while handling provider-specific configurations.","## Overview","The model factory (`agents_mcp_usage/factory/model_factory.py`) replaces manual string parsing with a configuration-driven approach that:","- Supports 13+ providers out of the box","- Leverages PydanticAI's native provider detection","- Handles provider-specific quirks via model profiles","- Provides an extensible design for adding new providers","## Usage","### Basic Usage","# Using provider:model format","agent = create_agent("," model=\"deepseek:deepseek-chat\","," mcp_servers=get_mcp_servers(),"," model_settings={\"temperature\": 0.7}","# Using native model names (automatically detected)"," model=\"gemini-2.5-pro\", # Google provider auto-detected"," mcp_servers=get_mcp_servers()","### Model String Formats","The factory supports multiple model string formats:","1. **Provider Prefix**: `provider:model`"," - `deepseek:deepseek-chat`"," - `openrouter:google/gemini-2.5-pro`"," - `github:xai/grok-3-mini`","2. **Native Models**: Direct names (provider auto-detected)"," - `gemini-2.5-pro` → Google"," - `gpt-4` → OpenAI"," - `claude-3-opus` → Anthropic","3. **Special Formats**:"," - Bedrock: `bedrock:us.amazon.nova-pro-v1:0`"," - Ollama: `ollama:llama3.2`","## Supported Providers","### Native PydanticAI Providers","These providers are handled directly by PydanticAI without special configuration:","- **Google**: Gemini models","- **OpenAI**: GPT models","- **Anthropic**: Claude models","### OpenAI-Compatible Providers","These providers use the OpenAI API format with custom endpoints:","| Provider | Env Variable | Example Model |","|----------|-------------|---------------|","| DeepSeek | `DEEPSEEK_API_KEY` | `deepseek:deepseek-chat` |","| OpenRouter | `OPENROUTER_API_KEY` | `openrouter:anthropic/claude-3.5-sonnet` |","| Perplexity | `PERPLEXITY_API_KEY` | `perplexity:sonar-pro` |","| Ollama | None (local) | `ollama:llama3.2` |","### OpenRouter (explicit provider handling)","This repo intentionally constructs an explicit OpenRouter provider/model for `openrouter:*` model strings, rather than relying on PydanticAI's implicit provider detection.","Implementation details:","- `openrouter:/` is parsed by [`parse_model_string()`](agents_mcp_usage/factory/model_factory.py:110) and handled in [`create_model()`](agents_mcp_usage/factory/model_factory.py:258) via the OpenRouter handler.","- The handler builds an OpenAI-compatible client pointed at `https://openrouter.ai/api/v1` and wraps it in PydanticAI's [`OpenRouterProvider`](agents_mcp_usage/factory/model_factory.py:222).","#### App attribution (optional)","OpenRouter supports attribution via headers.","Set these environment variables (optional):","export OPENROUTER_APP_URL=\"https://your-app.com\" # sent as HTTP-Referer","export OPENROUTER_APP_TITLE=\"Your App\" # sent as X-Title","Or pass overrides via `provider_kwargs`:"," model=\"openrouter:anthropic/claude-3.5-sonnet\","," provider_kwargs={"," \"api_key\": \"...\","," \"app_url\": \"https://your-app.com\","," \"app_title\": \"Your App\",","### Cloud Providers","| Provider | Configuration | Example Model |","|----------|--------------|---------------|","| AWS Bedrock | AWS credentials | `bedrock:us.amazon.nova-pro-v1:0` |","| Azure AI | Endpoint + API key | `azure:gpt-4` |","| Heroku AI | `HEROKU_INFERENCE_KEY` | `heroku:claude-3-sonnet` |","### Specialized Providers","| GitHub Models | `GITHUB_API_KEY` | `github:xai/grok-3-mini` |","| Grok (xAI) | `XAI_API_KEY` | `grok:grok-2-1212` |","| Fireworks | `FIREWORKS_API_KEY` | `fireworks:accounts/fireworks/models/qwq-32b` |","| Together | `TOGETHER_API_KEY` | `together:meta-llama/Llama-3.3-70B` |","## Model Profiles","Model profiles handle provider-specific behaviors and limitations:","profile = OpenAIModelProfile("," # Some providers don't support JSON schema references"," json_schema_transformer=InlineDefsJsonSchemaTransformer,"," # Some providers don't support strict tool definitions"," openai_supports_strict_tool_definition=False","### Providers Requiring Profiles","- DeepSeek: Requires inline JSON schemas","- Ollama: Limited tool definition support","## Adding New Providers","To add a new provider, update the `PROVIDER_CONFIGS` dictionary:","PROVIDER_CONFIGS = {"," \"new_provider\": {"," \"provider_class\": \"pydantic_ai.providers.new_provider.NewProvider\","," \"env_var\": \"NEW_PROVIDER_API_KEY\","," \"base_url\": \"https://api.newprovider.com\", # Optional"," \"profile\": { # Optional"," \"json_schema_transformer\": InlineDefsJsonSchemaTransformer,"," \"openai_supports_strict_tool_definition\": False","### Components","1. **parse_model_string()**: Extracts provider and model name from string","2. **create_model()**: Creates appropriate model instance or returns string for PydanticAI","3. **create_agent()**: Convenience function to create agents with proper model handling","4. **PROVIDER_CONFIGS**: Central configuration for all providers","### Flow","1. Model string is parsed to identify provider","2. If native provider (Google/OpenAI/Anthropic), return string for PydanticAI","3. If configured provider, create appropriate model instance","4. If unknown provider, return string and let PydanticAI handle it","5. Apply model profiles if specified in configuration","## Benefits","1. **Centralized Configuration**: All provider settings in one place","2. **Extensibility**: Easy to add new providers","3. **Consistency**: Same interface regardless of provider","4. **Flexibility**: Supports both native and custom providers","5. **Type Safety**: Leverages PydanticAI's type system","## Example: Multi-Provider Comparison","models = ["," \"gemini-2.5-pro\","," \"openai:gpt-4\","," \"deepseek:deepseek-chat\","," \"ollama:llama3.2\"","]","for model in models:"," mcp_servers=get_mcp_servers()"," result = await agent.run(\"Solve this problem...\")"," print(f\"{model}: {result}\")","## Environment Variables","Most providers require API keys via environment variables:","# Native providers","export GEMINI_API_KEY=\"...\"","export OPENAI_API_KEY=\"...\"","export ANTHROPIC_API_KEY=\"...\""," # Extended providers"," export DEEPSEEK_API_KEY=\"...\""," export OPENROUTER_API_KEY=\"...\""," export OPENROUTER_APP_URL=\"...\" # optional (app attribution)"," export OPENROUTER_APP_TITLE=\"...\" # optional (app attribution)"," export GITHUB_API_KEY=\"...\""," export XAI_API_KEY=\"...\""," export PERPLEXITY_API_KEY=\"...\"","# AWS Bedrock","export AWS_REGION=\"us-east-1\"","export AWS_PROFILE=\"default\"","## Migration Guide","### Old Approach","if model.startswith(\"bedrock:\"):"," from pydantic_ai.models.bedrock import BedrockConverseModel"," from pydantic_ai.providers.bedrock import BedrockProvider"," model_name = model.replace(\"bedrock:\", \"\")"," bedrock_model = BedrockConverseModel("," model_name,"," provider=BedrockProvider("," region_name=os.getenv(\"AWS_REGION\", \"us-east-1\"),"," profile_name=os.getenv(\"AWS_PROFILE\", \"default\")"," agent = Agent(bedrock_model, ...)","### New Approach"," model=\"bedrock:us.amazon.nova-pro-v1:0\",","The model factory handles all the provider-specific logic internally, providing a cleaner and more maintainable approach.",[5263],{"_2587":990,"_2588":5264,"_673":5265,"_2591":57},431,[3251,5266,315,5267,5268,3251,315,2592,2930,5269,5270,5271,5272,5273,5274,315,315,5275,5276,5277,5278,5279,5280,5281,5282,5283,2923,315,5284,5194,5285,5286,5287,5288,5289,5290,2733,2746,5291,5292,5293,5294,5295,5296,5200,5201,2733,2746,5297,5298,5293,5299,5300,2746,5301,5302,5303,5304,5305,5288,5306,5307,2730,2690,2923,315,315,5308,5309,315,5310,5311,5312,315,5313,5314,5315,5316,2621,5317,5318,5319,315,5320,5321,5322,315,5323,315,315,5324,5325,315,315,5326,5327,315,5328,2621,315,5329,5330,5331,315,5332,5333,5334,315,315,5335,2621,5336,2963,2617,5337,2963,2619,5338,2621,5339,5340,5341,2963,5342,5343,5344,5345,5346,5347,5348,2963,5349,5350,315,315,5351,5352,5353,5354,5355,5250,5251,2963,5356,5357,2963,5358,5359,2963,5360,5361,5362,2608,2963,5363,5254,5364,2608,315,315,5365,5366,5352,5353,5354,5367,5368,5357,2963,5369,5370,2963,5371,5372,5373,2963,5374,5375,5376,5377,5378,5379,5380,2963,5381,5382,2963,5383,5384,5385,5386,5387,5388,5389,2653,3090,5386,5387,5388,2653,315,315,5390,5352,5391,5354,5392,315,5393,5394,5395,2621,315,5396,5397,5398,5399,5400,5401,5402,315,5403,5404,5405,5406,5407,315,5408,5409,315,5410,5411,5412,5413,315,5414,2689,315,5415,5416,5417,315,5418,2672,5419,5420,5421,5422,5423,5424,5425,5423,5426,5427,5423,5428,5429,5430,5431,5430,5432,5433,5434,5435,5436,5437,5438,5439,5440,5441,5442,2676,315,2672,5443,5444,5445,5446,2676,315,5447,315,5448,5357,315,5449,5450,5451,5452,5453,2653,315,5454,5455,315,5456,5457,5458,5459,5460,5461,5462,5463,5464,315,5465,5466,5467,5468,5469,2608,5470,315,5471,315,315,5472,5473,5474,5353,5475,2621,5476,2963,5477,5478,5479,5480,2963,2617,5481,5482,5483,2963,2619,5484,2621,5485,5486,315,5320,2963,5487,5488,5319,2963,5489,5490,5491,5319,2963,5492,5493,5494,5495,5496,5497,5498,5499,5500,5501,4156,5502,5319,2963,5503,5504,5505,5506,3057,5507,5508,5509,5510,5511,5512,2963,5513,5323,315,315,4193,5514,5515,5474,5391,5516,4195,2621,5517,2963,5518,5519,5520,5521,2963,2617,5522,5523,5524,5525,5526,2963,2619,5527,2621,5528,5529,315,5530,5531,5532,315,5533,5534,4406,5535,5536,5537,5538,4120,5539,315,5540,315,5541,5542,5543,5544,5545,2608],"Model Factory for PydanticAI","This module provides a centralized way to create models across different providers,","leveraging PydanticAI's built-in provider support and handling special cases.","from typing import Any, Dict, Optional, Union","from pydantic_ai import Agent, InlineDefsJsonSchemaTransformer","from pydantic_ai.models import Model","from pydantic_ai.models.openai import OpenAIResponsesModel","from pydantic_ai.profiles.openai import OpenAIModelProfile","from pydantic_ai.providers.openai import OpenAIProvider","# Providers that PydanticAI handles natively with provider:model syntax.","#","# NOTE:","# - `openrouter:*` is intentionally **not** treated as native in this repo.","# We construct an explicit OpenRouter provider + OpenAI-compatible model so we","# can control configuration and avoid relying on implicit provider detection.","NATIVE_PROVIDERS = {"," \"deepseek\", \"github\", \"grok\", \"azure\","," \"fireworks\", \"together\", \"heroku\"","# Special cases that need custom handling"," \"bedrock\": {"," # Bedrock uses a different model class"," \"handler\": \"bedrock_handler\","," \"env_vars\": {"," \"region\": \"AWS_REGION\","," \"profile\": \"AWS_PROFILE\""," \"ollama\": {"," # Ollama is OpenAI-compatible but needs custom base URL"," \"handler\": \"openai_compatible_handler\","," \"base_url\": \"http://localhost:11434/v1\","," \"api_key\": \"not-needed\","," \"profile\": {"," \"perplexity\": {"," # Perplexity is OpenAI-compatible with custom base URL"," \"base_url\": \"https://api.perplexity.ai\","," \"env_var\": \"PERPLEXITY_API_KEY\""," \"openrouter\": {"," # OpenRouter has first-class support in PydanticAI, but we construct the"," # provider/model explicitly so configuration is controlled by this repo."," \"handler\": \"openrouter_handler\","," \"env_var\": \"OPENROUTER_API_KEY\","," \"app_url\": \"OPENROUTER_APP_URL\","," \"app_title\": \"OPENROUTER_APP_TITLE\",","def ensure_openai_responses_model(model_string: str) -> str:"," \"\"\"Ensure OpenAI models use the Responses API."," This helper normalises any OpenAI model identifier to the"," `openai-responses:` provider namespace so that PydanticAI"," will use the OpenAI Responses API rather than Chat Completions."," Examples:"," \"gpt-5\" -> \"openai-responses:gpt-5\""," \"openai:gpt-5-mini\" -> \"openai-responses:gpt-5-mini\""," \"openai-responses:gpt-5\" -> \"openai-responses:gpt-5\" (unchanged)"," # Avoid double-prefixing if already using Responses API directly"," if model_string.startswith(\"openai-responses:\"):"," return model_string"," provider, model_name = parse_model_string(model_string)"," if provider == \"openai\":"," return f\"openai-responses:{model_name}\""," return model_string","# Accept optional reasoning hints for models that support it","REASONING_SUFFIX_RE = re.compile(r\"\\s*\\((low|medium|high|none|minimal|xhigh)\\)\\s*$\", re.IGNORECASE)","def extract_reasoning_effort(model_string: str) -> tuple[str, Optional[str]]:"," \"\"\"Strip trailing reasoning hint like \"(medium)\" or \"(none)\" from model string."," Returns the base model string and the effort level (lowercased) if present."," match = REASONING_SUFFIX_RE.search(model_string)"," if not match:"," return model_string.strip(), None"," effort = match.group(1).lower()"," base = REASONING_SUFFIX_RE.sub(\"\", model_string).strip()"," return base, effort","def parse_model_string(model_string: str) -> tuple[Optional[str], str]:"," Parse a model string to extract provider and model name."," model_string: Model identifier (e.g., \"openai:gpt-4\", \"deepseek:deepseek-chat\", \"gemini-2.5-pro\")"," Tuple of (provider, model_name)"," if \":\" in model_string:"," provider, model_name = model_string.split(\":\", 1)"," return provider, model_name"," # Check for known prefixes without colon"," if model_string.startswith(\"gemini-\"):"," return \"google\", model_string"," elif model_string.startswith(\"gpt-\") or model_string.startswith(\"o1-\") or model_string.startswith(\"o3-\") or model_string.startswith(\"o4-\"):"," return \"openai\", model_string"," elif model_string.startswith(\"claude-\"):"," return \"anthropic\", model_string"," # Default to no specific provider"," return None, model_string","def handle_bedrock_model("," model_name: str,"," provider_kwargs: Optional[Dict[str, Any]] = None",") -> Model:"," \"\"\"Handle AWS Bedrock models which require special treatment.\"\"\""," config = PROVIDER_CONFIGS[\"bedrock\"]"," provider_kwargs = provider_kwargs or {}"," region = provider_kwargs.get(\"region_name\") or os.getenv(config[\"env_vars\"][\"region\"], \"us-east-1\")"," profile = provider_kwargs.get(\"profile_name\") or os.getenv(config[\"env_vars\"][\"profile\"], \"default\")"," provider_instance = BedrockProvider("," region_name=region,"," profile_name=profile"," return BedrockConverseModel("," provider=provider_instance","def handle_openai_compatible("," provider: str,"," \"\"\"Handle OpenAI-compatible providers that need custom configuration.\"\"\""," config = PROVIDER_CONFIGS[provider]"," # Build OpenAI provider kwargs"," openai_kwargs = {}"," # Set base URL"," if \"base_url\" in config:"," openai_kwargs[\"base_url\"] = provider_kwargs.get(\"base_url\", config[\"base_url\"])"," # Handle API key"," if \"api_key\" in config:"," openai_kwargs[\"api_key\"] = config[\"api_key\"]"," elif \"env_var\" in config:"," api_key = provider_kwargs.get(\"api_key\") or os.getenv(config[\"env_var\"])"," if api_key:"," openai_kwargs[\"api_key\"] = api_key"," # Create OpenAI provider"," provider_instance = OpenAIProvider(**openai_kwargs)"," # Create model with profile if specified"," if config.get(\"profile\"):"," profile = OpenAIModelProfile(**config[\"profile\"])"," return OpenAIResponsesModel("," model_name,"," provider=provider_instance,"," profile=profile,","def handle_openrouter_model("," provider_kwargs: Optional[Dict[str, Any]] = None,"," \"\"\"Handle OpenRouter models via an explicit provider/model."," PydanticAI can accept `openrouter:` strings directly, but we build"," the provider/model explicitly so this repository owns configuration and can"," support optional app attribution."," # Import lazily so users without the optional dependency can still import"," # the rest of the repo."," #"," # NOTE: In pydantic-ai-slim==1.17.x, OpenRouter uses the OpenAI-compatible"," # models (Chat Completions) with an `OpenRouterProvider`, rather than a"," # dedicated `OpenRouterModel` class."," import json"," from openai import AsyncOpenAI"," from openai.types import chat"," from pydantic import ValidationError"," from pydantic_ai.models.openai import OpenAIChatModel"," from pydantic_ai.providers.openrouter import OpenRouterProvider"," class OpenRouterPatchedChatModel(OpenAIChatModel):"," \"\"\"OpenAIChatModel with OpenRouter response sanitation."," Some OpenRouter-backed models (notably Anthropic) occasionally return"," invalid tool-call payloads such as `function.arguments = null`. PydanticAI"," validates the OpenAI response strictly, which raises"," `UnexpectedModelBehavior` before tool execution can happen."," We patch the raw ChatCompletion dict prior to Pydantic validation."," def _process_response(self, response: chat.ChatCompletion | str): # type: ignore[override]"," if isinstance(response, chat.ChatCompletion):"," data = response.model_dump()"," # Fix invalid tool_calls payloads (e.g. `arguments: null`)"," choices = data.get(\"choices\")"," if isinstance(choices, list):"," for choice in choices:"," if not isinstance(choice, dict):"," continue"," message = choice.get(\"message\")"," if not isinstance(message, dict):"," tool_calls = message.get(\"tool_calls\")"," if not isinstance(tool_calls, list):"," for call in tool_calls:"," if not isinstance(call, dict):"," continue"," if call.get(\"type\") != \"function\":"," fn = call.get(\"function\")"," if not isinstance(fn, dict):"," fn = {}"," call[\"function\"] = fn"," args = fn.get(\"arguments\")"," if args is None:"," fn[\"arguments\"] = \"{}\""," elif not isinstance(args, str):"," fn[\"arguments\"] = json.dumps(args)"," except Exception:"," # Best-effort only; fall through to default handling."," response = chat.ChatCompletion.model_validate(data)"," except ValidationError:"," # If sanitation didn't help, let the base model raise a"," # helpful UnexpectedModelBehavior."," return super()._process_response(response)"," config = PROVIDER_CONFIGS[\"openrouter\"]"," api_key = provider_kwargs.get(\"api_key\") or os.getenv(config[\"env_var\"])"," if not api_key:"," raise ValueError("," \"OpenRouter API key not configured. \""," \"Set OPENROUTER_API_KEY or pass provider_kwargs={'api_key': '...'}\""," app_url = provider_kwargs.get(\"app_url\") or os.getenv(config[\"env_vars\"][\"app_url\"])"," app_title = provider_kwargs.get(\"app_title\") or os.getenv(config[\"env_vars\"][\"app_title\"])"," # OpenRouter app attribution uses HTTP headers:"," # - HTTP-Referer: your app URL"," # - X-Title: your app name"," # See: https://openrouter.ai/docs/app-attribution"," default_headers: dict[str, str] = {}"," if app_url:"," default_headers[\"HTTP-Referer\"] = app_url"," if app_title:"," default_headers[\"X-Title\"] = app_title"," base_url = provider_kwargs.get(\"base_url\") or \"https://openrouter.ai/api/v1\""," openai_client = AsyncOpenAI("," base_url=base_url,"," api_key=api_key,"," default_headers=default_headers or None,"," provider_instance = OpenRouterProvider(openai_client=openai_client)"," return OpenRouterPatchedChatModel(model_name, provider=provider_instance)","def create_model("," model_string: str,"," model_settings: Optional[Dict[str, Any]] = None,",") -> Union[Model, str]:"," Create a model instance from a model string."," This function:"," 1. Returns the string directly for providers PydanticAI handles natively"," 2. Handles special cases like Bedrock and OpenAI-compatible providers"," 3. Falls back to returning the string for unknown providers"," model_string: Model identifier"," model_settings: Optional model-specific settings"," provider_kwargs: Optional provider-specific kwargs (e.g., azure_endpoint)"," Either a Model instance or the original string for PydanticAI to handle"," # First, normalise any OpenAI models to use the Responses API"," model_string = ensure_openai_responses_model(model_string)"," # No provider prefix - let PydanticAI handle it"," if provider is None:"," # Standard providers that PydanticAI auto-detects"," # Note: OpenAI models have been converted to \"openai-responses:*\" above"," if provider in [\"google\", \"anthropic\"]:"," # Providers with native PydanticAI support"," if provider in NATIVE_PROVIDERS:"," # Special validation for Azure"," if provider == \"azure\" and provider_kwargs:"," required = [\"azure_endpoint\", \"api_version\"]"," missing = [k for k in required if k not in provider_kwargs]"," if missing:"," raise ValueError("," f\"Azure provider requires {missing} in provider_kwargs. \""," f\"Alternatively, let PydanticAI handle it by using the string directly.\""," # Return the string - PydanticAI will handle it"," # Handle special cases"," if provider in PROVIDER_CONFIGS:"," config = PROVIDER_CONFIGS[provider]"," handler = config.get(\"handler\")"," if handler == \"bedrock_handler\":"," return handle_bedrock_model(model_name, provider_kwargs)"," elif handler == \"openai_compatible_handler\":"," return handle_openai_compatible(provider, model_name, provider_kwargs)"," elif handler == \"openrouter_handler\":"," return handle_openrouter_model(model_name, provider_kwargs)"," # Unknown provider - return string and let PydanticAI try"," model: Union[str, Model],"," mcp_servers=None,"," **agent_kwargs"," Create an Agent with proper model handling."," This is a convenience function that:"," 1. Converts model strings to appropriate Model instances when needed"," 2. Passes through Model instances unchanged"," 3. Leverages PydanticAI's native provider support"," model: Model string or Model instance"," mcp_servers: Optional MCP servers"," model_settings: Optional model settings"," provider_kwargs: Optional provider-specific kwargs"," **agent_kwargs: Additional Agent kwargs"," Configured Agent instance"," if isinstance(model, str):"," base_model, reasoning_effort = extract_reasoning_effort(model)"," # Ensure we have a dict to attach model-level settings"," if model_settings is None:"," model_settings = {}"," # Only apply reasoning hint to OpenAI Responses-capable models"," provider, _ = parse_model_string(base_model)"," reasoning_effort"," and isinstance(model_settings, dict)"," and provider in {\"openai\", \"openai-responses\"}"," and \"openai_reasoning_effort\" not in model_settings"," model_settings[\"openai_reasoning_effort\"] = reasoning_effort"," model = create_model(base_model, model_settings, provider_kwargs)"," return Agent("," model,"," mcp_servers=mcp_servers,"," model_settings=model_settings if isinstance(model, str) else None,"," 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Pricing inputs updated in `costs.json`; include the new OpenRouter env var template (`OPENROUTER_API_KEY`).",18,"\n- `merge_benchmark_results.py` outputs the same schema as `preprocess_merbench_data.py` (stats/leaderboard/pareto/raw_data/etc.).",378,444,"\n- Optional per-case debug traces are emitted to `/debug_traces/` when enabled, containing full prompts and tool-call payloads (sensitive data).",1151,1219,60,70,"\n\n## Schema / Output Impacts\n- Merbench CSV output now includes a request-usage column (`Requests_Used`).","\n- Verify retry orchestration logic (offline stub runner):\n - `uv run agents_mcp_usage/evaluations/mermaid_evals/scripts/verify_retry_orchestration.py`",116,124,"\n- Merge processed Merbench outputs:\n - `uv run agents_mcp_usage/evaluations/mermaid_evals/scripts/merge_benchmark_results.py -i results/run1.json results/run2.json -o results/merged.json --dedup keep-all --report merge_report.json`",80,90,"\n- Run multi-model Merbench with OpenRouter:\n - `uv run agents_mcp_usage/evaluations/mermaid_evals/run_multi_evals.py --models \"openrouter:anthropic/claude-3.7-sonnet,gemini-3-flash-preview\" --runs 5 --sequential`",234,246,"\n\n## How to Run / Verify\n- Run the new model-factory demo:\n - `uv run agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp_factory.py --list-models`",23,145,319,516,122,"\n- Merbench debugging and reporting improvements (debug traces + request usage tracking + cost normalization) make it easier to diagnose tool loops, retries, and cost accuracy across providers.",200,"\n\n## Why\n- Centralizing provider/model creation (including OpenRouter) makes multi-provider demos and evaluation runs consistent and configurable while enabling OpenAI Responses normalization and provider-specific handling in one place.",88,"\n- Refresh model pricing data, add OpenRouter env configuration, and pin the Mermaid CLI version used by the validator.",240,"\n- Add Merbench utilities for merging processed benchmark outputs and verifying retry orchestration, plus preprocessing updates to normalize model names for cost lookups.","\n- Expand Merbench evaluation tooling with per-case debug trace capture, new request-usage tracking, and updated evaluation guidance (including OpenRouter examples).",228,253,"Below is a ready-to-paste PR description based on the changes in this branch.\n\n---\n\n**Proposed PR Title**\n- **feat: add model factory + merbench eval updates**\n\n**PR Body**\n## Summary\n- Introduce a centralized model factory with explicit provider handling (including OpenRouter) plus a new demo and documentation for multi-provider usage in the basic MCP 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